pesuacademy.models package

Submodules

pesuacademy.models.announcement module

Model for announcements in the PESU Academy system.

class pesuacademy.models.announcement.Announcement(**data)

Bases: pydantic.main.BaseModel

Represents an announcement in the PESU Academy system.

Parameters:

• title (str)

• date (datetime.date)

• content (str)

• attachments (list[str] | None)

title

The title of the announcement.

Type:

str

date

The date of the announcement.

Type:

datetime.date

content

The content of the announcement.

Type:

str

attachments

Optional list of attachment links related to the announcement.

Type:

Optional[List[str]]

title: str

date: date

content: str

attachments: list[str] | None

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

Parameters:

memo (dict[int, Any] | None)

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

• core_schema (Union[InvalidSchema, AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, ArgumentsV3Schema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.

• handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

dict[str, Any]

Returns:

A JSON schema, as a Python object.

__init__(**data)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

data (Any)

Return type:

None

__iter__()

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__pretty__(fmt, **kwargs)

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:

Generator[Any, None, None]

Parameters:

• fmt (Callable[[Any], Any])

• kwargs (Any)

classmethod __pydantic_init_subclass__(**kwargs)

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:

**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.

Return type:

None

__repr_name__()

Name of the instance’s class, used in __repr__.

Return type:

str

__repr_recursion__(object)

Returns the string representation of a recursive object.

Return type:

str

Parameters:

object (Any)

__rich_repr__()

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:

Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

classmethod construct(_fields_set=None, **values)

Return type:

Self

Parameters:

• _fields_set (set[str] | None)

• values (Any)

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

• include – Optional set or mapping specifying which fields to include in the copied model.

• exclude – Optional set or mapping specifying which fields to exclude in the copied model.

• update – Optional dictionary of field-value pairs to override field values in the copied model.

• deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

Return type:

Self

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Return type:

Dict[str, Any]

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

classmethod from_orm(obj)

Return type:

Self

Parameters:

obj (Any)

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)

Return type:

str

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

• encoder (Callable[[Any], Any] | None)

• models_as_dict (bool)

• dumps_kwargs (Any)

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

• _fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

• values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

!!! abstract “Usage Documentation”

[model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update (Mapping[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#modelmodel_dump)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

• mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to use the field’s alias in the dictionary key if defined.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

dict[str, Any]

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

• indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to serialize using field aliases.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

str

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'attachments': FieldInfo(annotation=Union[list[str], NoneType], required=False, default=None), 'content': FieldInfo(annotation=str, required=True), 'date': FieldInfo(annotation=date, required=True), 'title': FieldInfo(annotation=str, required=True)}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')

Generates a JSON schema for a model class.

Parameters:

• by_alias (bool) – Whether to use attribute aliases or not.

• ref_template (str) – The reference template.

• schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

• mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Return type:

str

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(context, /)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:

None

Parameters:

context (Any)

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

• force (bool) – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors (bool) – Whether to raise errors, defaults to True.

• _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

• _types_namespace (Mapping[str, Any] | None) – The types namespace, defaults to None.

Return type:

bool | None

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)

Validate a pydantic model instance.

Parameters:

• obj (Any) – The object to validate.

• strict (bool | None) – Whether to enforce types strictly.

• from_attributes (bool | None) – Whether to extract data from object attributes.

• context (Any | None) – Additional context to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data (str | bytes | bytearray) – The JSON data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)

Validate the given object with string data against the Pydantic model.

Parameters:

• obj (Any) – The object containing string data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• path (str | pathlib.Path)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod parse_obj(obj)

Return type:

Self

Parameters:

obj (Any)

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• b (str | bytes)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')

Return type:

Dict[str, Any]

Parameters:

• by_alias (bool)

• ref_template (str)

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)

Return type:

str

Parameters:

• by_alias (bool)

• ref_template (str)

• dumps_kwargs (Any)

classmethod update_forward_refs(**localns)

Return type:

None

Parameters:

localns (Any)

classmethod validate(value)

Return type:

Self

Parameters:

value (Any)

pesuacademy.models.course module

Model for courses in the PESU Academy system.

class pesuacademy.models.course.Attendance(**data)

Bases: pydantic.main.BaseModel

Represents attendance information for a course.

Parameters:

• attended (int | None)

• total (int | None)

• percentage (float | None)

attended

Number of classes attended by the student.

Type:

Optional[int]

total

Total number of classes for the course.

Type:

Optional[int]

percentage

Attendance percentage calculated from attended and total classes.

Type:

Optional[float]

attended: int | None

total: int | None

percentage: float | None

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

Parameters:

memo (dict[int, Any] | None)

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

• core_schema (Union[InvalidSchema, AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, ArgumentsV3Schema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.

• handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

dict[str, Any]

Returns:

A JSON schema, as a Python object.

__init__(**data)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

data (Any)

Return type:

None

__iter__()

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__pretty__(fmt, **kwargs)

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:

Generator[Any, None, None]

Parameters:

• fmt (Callable[[Any], Any])

• kwargs (Any)

classmethod __pydantic_init_subclass__(**kwargs)

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:

**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.

Return type:

None

__repr_name__()

Name of the instance’s class, used in __repr__.

Return type:

str

__repr_recursion__(object)

Returns the string representation of a recursive object.

Return type:

str

Parameters:

object (Any)

__rich_repr__()

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:

Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

classmethod construct(_fields_set=None, **values)

Return type:

Self

Parameters:

• _fields_set (set[str] | None)

• values (Any)

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

• include – Optional set or mapping specifying which fields to include in the copied model.

• exclude – Optional set or mapping specifying which fields to exclude in the copied model.

• update – Optional dictionary of field-value pairs to override field values in the copied model.

• deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Return type:

Dict[str, Any]

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

classmethod from_orm(obj)

Return type:

Self

Parameters:

obj (Any)

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)

Return type:

str

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

• encoder (Callable[[Any], Any] | None)

• models_as_dict (bool)

• dumps_kwargs (Any)

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

• _fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

• values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

!!! abstract “Usage Documentation”

[model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update (Mapping[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#modelmodel_dump)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

• mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to use the field’s alias in the dictionary key if defined.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

dict[str, Any]

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

• indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to serialize using field aliases.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

str

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'attended': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'percentage': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'total': FieldInfo(annotation=Union[int, NoneType], required=False, default=None)}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')

Generates a JSON schema for a model class.

Parameters:

• by_alias (bool) – Whether to use attribute aliases or not.

• ref_template (str) – The reference template.

• schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

• mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Return type:

str

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(context, /)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:

None

Parameters:

context (Any)

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

• force (bool) – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors (bool) – Whether to raise errors, defaults to True.

• _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

• _types_namespace (Mapping[str, Any] | None) – The types namespace, defaults to None.

Return type:

bool | None

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)

Validate a pydantic model instance.

Parameters:

• obj (Any) – The object to validate.

• strict (bool | None) – Whether to enforce types strictly.

• from_attributes (bool | None) – Whether to extract data from object attributes.

• context (Any | None) – Additional context to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data (str | bytes | bytearray) – The JSON data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)

Validate the given object with string data against the Pydantic model.

Parameters:

• obj (Any) – The object containing string data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• path (str | pathlib.Path)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod parse_obj(obj)

Return type:

Self

Parameters:

obj (Any)

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• b (str | bytes)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')

Return type:

Dict[str, Any]

Parameters:

• by_alias (bool)

• ref_template (str)

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)

Return type:

str

Parameters:

• by_alias (bool)

• ref_template (str)

• dumps_kwargs (Any)

classmethod update_forward_refs(**localns)

Return type:

None

Parameters:

localns (Any)

classmethod validate(value)

Return type:

Self

Parameters:

value (Any)

class pesuacademy.models.course.Course(**data)

Bases: pydantic.main.BaseModel

Represents a course in the PESU Academy system.

Parameters:

• code (str)

• title (str)

• type (str | None)

• status (str | None)

• attendance (pesuacademy.models.course.Attendance | None)

• id (str | None)

code

Unique identifier for the course.

Type:

str

title

Title of the course.

Type:

str

type

Type of the course (e.g., FC, CC).

Type:

Optional[str]

status

Current status of the course (e.g., Enrolled).

Type:

Optional[str]

attendance

Attendance information for the course.

Type:

Optional[Attendance]

id

Unique identifier for the course instance.

Type:

Optional[str]

code: str

title: str

type: str | None

status: str | None

attendance: Attendance | None

id: str | None

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

Parameters:

memo (dict[int, Any] | None)

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

• core_schema (Union[InvalidSchema, AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, ArgumentsV3Schema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.

• handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

dict[str, Any]

Returns:

A JSON schema, as a Python object.

__init__(**data)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

data (Any)

Return type:

None

__iter__()

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__pretty__(fmt, **kwargs)

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:

Generator[Any, None, None]

Parameters:

• fmt (Callable[[Any], Any])

• kwargs (Any)

classmethod __pydantic_init_subclass__(**kwargs)

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:

**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.

Return type:

None

__repr_name__()

Name of the instance’s class, used in __repr__.

Return type:

str

__repr_recursion__(object)

Returns the string representation of a recursive object.

Return type:

str

Parameters:

object (Any)

__rich_repr__()

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:

Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

classmethod construct(_fields_set=None, **values)

Return type:

Self

Parameters:

• _fields_set (set[str] | None)

• values (Any)

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

• include – Optional set or mapping specifying which fields to include in the copied model.

• exclude – Optional set or mapping specifying which fields to exclude in the copied model.

• update – Optional dictionary of field-value pairs to override field values in the copied model.

• deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Return type:

Dict[str, Any]

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

classmethod from_orm(obj)

Return type:

Self

Parameters:

obj (Any)

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)

Return type:

str

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

• encoder (Callable[[Any], Any] | None)

• models_as_dict (bool)

• dumps_kwargs (Any)

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

• _fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

• values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

!!! abstract “Usage Documentation”

[model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update (Mapping[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#modelmodel_dump)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

• mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to use the field’s alias in the dictionary key if defined.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

dict[str, Any]

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

• indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to serialize using field aliases.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

str

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'attendance': FieldInfo(annotation=Union[Attendance, NoneType], required=False, default=None), 'code': FieldInfo(annotation=str, required=True), 'id': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'status': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'title': FieldInfo(annotation=str, required=True), 'type': FieldInfo(annotation=Union[str, NoneType], required=False, default=None)}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')

Generates a JSON schema for a model class.

Parameters:

• by_alias (bool) – Whether to use attribute aliases or not.

• ref_template (str) – The reference template.

• schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

• mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Return type:

str

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(context, /)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:

None

Parameters:

context (Any)

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

• force (bool) – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors (bool) – Whether to raise errors, defaults to True.

• _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

• _types_namespace (Mapping[str, Any] | None) – The types namespace, defaults to None.

Return type:

bool | None

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)

Validate a pydantic model instance.

Parameters:

• obj (Any) – The object to validate.

• strict (bool | None) – Whether to enforce types strictly.

• from_attributes (bool | None) – Whether to extract data from object attributes.

• context (Any | None) – Additional context to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data (str | bytes | bytearray) – The JSON data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)

Validate the given object with string data against the Pydantic model.

Parameters:

• obj (Any) – The object containing string data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• path (str | pathlib.Path)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod parse_obj(obj)

Return type:

Self

Parameters:

obj (Any)

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• b (str | bytes)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')

Return type:

Dict[str, Any]

Parameters:

• by_alias (bool)

• ref_template (str)

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)

Return type:

str

Parameters:

• by_alias (bool)

• ref_template (str)

• dumps_kwargs (Any)

classmethod update_forward_refs(**localns)

Return type:

None

Parameters:

localns (Any)

classmethod validate(value)

Return type:

Self

Parameters:

value (Any)

pesuacademy.models.materials module

Model for materials in the PESU Academy system.

class pesuacademy.models.materials.Unit(**data)

Bases: pydantic.main.BaseModel

Represents a unit of a course in the PESU Academy system.

Parameters:

• title (str)

• id (str)

title

The title of the unit.

Type:

str

unit_id

Unique identifier for the unit.

Type:

str

title: str

id: str

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

Parameters:

memo (dict[int, Any] | None)

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

• core_schema (Union[InvalidSchema, AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, ArgumentsV3Schema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.

• handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

dict[str, Any]

Returns:

A JSON schema, as a Python object.

__init__(**data)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

data (Any)

Return type:

None

__iter__()

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__pretty__(fmt, **kwargs)

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:

Generator[Any, None, None]

Parameters:

• fmt (Callable[[Any], Any])

• kwargs (Any)

classmethod __pydantic_init_subclass__(**kwargs)

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:

**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.

Return type:

None

__repr_name__()

Name of the instance’s class, used in __repr__.

Return type:

str

__repr_recursion__(object)

Returns the string representation of a recursive object.

Return type:

str

Parameters:

object (Any)

__rich_repr__()

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:

Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

classmethod construct(_fields_set=None, **values)

Return type:

Self

Parameters:

• _fields_set (set[str] | None)

• values (Any)

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

• include – Optional set or mapping specifying which fields to include in the copied model.

• exclude – Optional set or mapping specifying which fields to exclude in the copied model.

• update – Optional dictionary of field-value pairs to override field values in the copied model.

• deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Return type:

Dict[str, Any]

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

classmethod from_orm(obj)

Return type:

Self

Parameters:

obj (Any)

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)

Return type:

str

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

• encoder (Callable[[Any], Any] | None)

• models_as_dict (bool)

• dumps_kwargs (Any)

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

• _fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

• values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

!!! abstract “Usage Documentation”

[model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update (Mapping[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#modelmodel_dump)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

• mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to use the field’s alias in the dictionary key if defined.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

dict[str, Any]

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

• indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to serialize using field aliases.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

str

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'id': FieldInfo(annotation=str, required=True), 'title': FieldInfo(annotation=str, required=True)}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')

Generates a JSON schema for a model class.

Parameters:

• by_alias (bool) – Whether to use attribute aliases or not.

• ref_template (str) – The reference template.

• schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

• mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Return type:

str

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(context, /)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:

None

Parameters:

context (Any)

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

• force (bool) – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors (bool) – Whether to raise errors, defaults to True.

• _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

• _types_namespace (Mapping[str, Any] | None) – The types namespace, defaults to None.

Return type:

bool | None

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)

Validate a pydantic model instance.

Parameters:

• obj (Any) – The object to validate.

• strict (bool | None) – Whether to enforce types strictly.

• from_attributes (bool | None) – Whether to extract data from object attributes.

• context (Any | None) – Additional context to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data (str | bytes | bytearray) – The JSON data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)

Validate the given object with string data against the Pydantic model.

Parameters:

• obj (Any) – The object containing string data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• path (str | pathlib.Path)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod parse_obj(obj)

Return type:

Self

Parameters:

obj (Any)

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• b (str | bytes)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')

Return type:

Dict[str, Any]

Parameters:

• by_alias (bool)

• ref_template (str)

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)

Return type:

str

Parameters:

• by_alias (bool)

• ref_template (str)

• dumps_kwargs (Any)

classmethod update_forward_refs(**localns)

Return type:

None

Parameters:

localns (Any)

classmethod validate(value)

Return type:

Self

Parameters:

value (Any)

class pesuacademy.models.materials.Topic(**data)

Bases: pydantic.main.BaseModel

Represents a topic within a unit in the PESU Academy system.

Parameters:

• title (str)

• id (str)

• course_id (str)

• unit_id (str)

title

The title of the topic.

Type:

str

topic_id

Unique identifier for the topic.

Type:

str

course_id

Identifier for the course this topic belongs to.

Type:

str

unit_id

Identifier for the unit this topic belongs to.

Type:

str

title: str

id: str

course_id: str

unit_id: str

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

Parameters:

memo (dict[int, Any] | None)

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

• core_schema (Union[InvalidSchema, AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, ArgumentsV3Schema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.

• handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

dict[str, Any]

Returns:

A JSON schema, as a Python object.

__init__(**data)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

data (Any)

Return type:

None

__iter__()

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__pretty__(fmt, **kwargs)

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:

Generator[Any, None, None]

Parameters:

• fmt (Callable[[Any], Any])

• kwargs (Any)

classmethod __pydantic_init_subclass__(**kwargs)

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:

**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.

Return type:

None

__repr_name__()

Name of the instance’s class, used in __repr__.

Return type:

str

__repr_recursion__(object)

Returns the string representation of a recursive object.

Return type:

str

Parameters:

object (Any)

__rich_repr__()

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:

Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

classmethod construct(_fields_set=None, **values)

Return type:

Self

Parameters:

• _fields_set (set[str] | None)

• values (Any)

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

• include – Optional set or mapping specifying which fields to include in the copied model.

• exclude – Optional set or mapping specifying which fields to exclude in the copied model.

• update – Optional dictionary of field-value pairs to override field values in the copied model.

• deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Return type:

Dict[str, Any]

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

classmethod from_orm(obj)

Return type:

Self

Parameters:

obj (Any)

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)

Return type:

str

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

• encoder (Callable[[Any], Any] | None)

• models_as_dict (bool)

• dumps_kwargs (Any)

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

• _fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

• values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

!!! abstract “Usage Documentation”

[model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update (Mapping[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#modelmodel_dump)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

• mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to use the field’s alias in the dictionary key if defined.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

dict[str, Any]

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

• indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to serialize using field aliases.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

str

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'course_id': FieldInfo(annotation=str, required=True), 'id': FieldInfo(annotation=str, required=True), 'title': FieldInfo(annotation=str, required=True), 'unit_id': FieldInfo(annotation=str, required=True)}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')

Generates a JSON schema for a model class.

Parameters:

• by_alias (bool) – Whether to use attribute aliases or not.

• ref_template (str) – The reference template.

• schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

• mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Return type:

str

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(context, /)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:

None

Parameters:

context (Any)

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

• force (bool) – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors (bool) – Whether to raise errors, defaults to True.

• _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

• _types_namespace (Mapping[str, Any] | None) – The types namespace, defaults to None.

Return type:

bool | None

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)

Validate a pydantic model instance.

Parameters:

• obj (Any) – The object to validate.

• strict (bool | None) – Whether to enforce types strictly.

• from_attributes (bool | None) – Whether to extract data from object attributes.

• context (Any | None) – Additional context to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data (str | bytes | bytearray) – The JSON data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)

Validate the given object with string data against the Pydantic model.

Parameters:

• obj (Any) – The object containing string data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• path (str | pathlib.Path)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod parse_obj(obj)

Return type:

Self

Parameters:

obj (Any)

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• b (str | bytes)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')

Return type:

Dict[str, Any]

Parameters:

• by_alias (bool)

• ref_template (str)

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)

Return type:

str

Parameters:

• by_alias (bool)

• ref_template (str)

• dumps_kwargs (Any)

classmethod update_forward_refs(**localns)

Return type:

None

Parameters:

localns (Any)

classmethod validate(value)

Return type:

Self

Parameters:

value (Any)

class pesuacademy.models.materials.MaterialLink(**data)

Bases: pydantic.main.BaseModel

Represents a link to a material in the PESU Academy system.

Parameters:

• title (str)

• url (str)

• is_pdf (bool)

title

The title of the material link.

Type:

str

url

The URL of the material.

Type:

str

is_pdf

Indicates if the link points to a PDF document.

Type:

bool

title: str

url: str

is_pdf: bool

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

Parameters:

memo (dict[int, Any] | None)

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

• core_schema (Union[InvalidSchema, AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, ArgumentsV3Schema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.

• handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

dict[str, Any]

Returns:

A JSON schema, as a Python object.

__init__(**data)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

data (Any)

Return type:

None

__iter__()

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__pretty__(fmt, **kwargs)

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:

Generator[Any, None, None]

Parameters:

• fmt (Callable[[Any], Any])

• kwargs (Any)

classmethod __pydantic_init_subclass__(**kwargs)

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:

**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.

Return type:

None

__repr_name__()

Name of the instance’s class, used in __repr__.

Return type:

str

__repr_recursion__(object)

Returns the string representation of a recursive object.

Return type:

str

Parameters:

object (Any)

__rich_repr__()

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:

Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

classmethod construct(_fields_set=None, **values)

Return type:

Self

Parameters:

• _fields_set (set[str] | None)

• values (Any)

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

• include – Optional set or mapping specifying which fields to include in the copied model.

• exclude – Optional set or mapping specifying which fields to exclude in the copied model.

• update – Optional dictionary of field-value pairs to override field values in the copied model.

• deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Return type:

Dict[str, Any]

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

classmethod from_orm(obj)

Return type:

Self

Parameters:

obj (Any)

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)

Return type:

str

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

• encoder (Callable[[Any], Any] | None)

• models_as_dict (bool)

• dumps_kwargs (Any)

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

• _fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

• values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

!!! abstract “Usage Documentation”

[model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update (Mapping[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#modelmodel_dump)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

• mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to use the field’s alias in the dictionary key if defined.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

dict[str, Any]

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

• indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to serialize using field aliases.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

str

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'is_pdf': FieldInfo(annotation=bool, required=True), 'title': FieldInfo(annotation=str, required=True), 'url': FieldInfo(annotation=str, required=True)}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')

Generates a JSON schema for a model class.

Parameters:

• by_alias (bool) – Whether to use attribute aliases or not.

• ref_template (str) – The reference template.

• schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

• mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Return type:

str

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(context, /)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:

None

Parameters:

context (Any)

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

• force (bool) – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors (bool) – Whether to raise errors, defaults to True.

• _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

• _types_namespace (Mapping[str, Any] | None) – The types namespace, defaults to None.

Return type:

bool | None

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)

Validate a pydantic model instance.

Parameters:

• obj (Any) – The object to validate.

• strict (bool | None) – Whether to enforce types strictly.

• from_attributes (bool | None) – Whether to extract data from object attributes.

• context (Any | None) – Additional context to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data (str | bytes | bytearray) – The JSON data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)

Validate the given object with string data against the Pydantic model.

Parameters:

• obj (Any) – The object containing string data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• path (str | pathlib.Path)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod parse_obj(obj)

Return type:

Self

Parameters:

obj (Any)

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• b (str | bytes)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')

Return type:

Dict[str, Any]

Parameters:

• by_alias (bool)

• ref_template (str)

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)

Return type:

str

Parameters:

• by_alias (bool)

• ref_template (str)

• dumps_kwargs (Any)

classmethod update_forward_refs(**localns)

Return type:

None

Parameters:

localns (Any)

classmethod validate(value)

Return type:

Self

Parameters:

value (Any)

pesuacademy.models.profile module

Model for student profile in the PESU Academy system.

class pesuacademy.models.profile.PersonalDetails(**data)

Bases: pydantic.main.BaseModel

Represents personal details of a user in the PESU Academy system.

Parameters:

• name (str)

• pesu_id (str)

• srn (str)

• program (str)

• branch (str)

• semester (str)

• section (str)

• email_id (str)

• contact_no (str)

• aadhar_no (str | None)

• name_as_in_aadhar (str | None)

• image (str | None)

name

Full name of the user.

Type:

str

pesu_id

PESU ID of the user. (PRN or similar)

Type:

str

srn

SRN of the user.

Type:

str

program

Program enrolled in by the user.

Type:

str

branch

Branch of study.

Type:

str

semester

Current semester.

Type:

str

section

Section of the user.

Type:

str

email_id

Email address of the user.

Type:

str

contact_no

Contact number of the user.

Type:

str

name_as_in_aadhar

Name as per Aadhar card.

Type:

str

image

Base64 encoded profile image, if available.

Type:

Optional[str]

name: str

pesu_id: str

srn: str

program: str

branch: str

semester: str

section: str

email_id: str

contact_no: str

aadhar_no: str | None

name_as_in_aadhar: str | None

image: str | None

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

Parameters:

memo (dict[int, Any] | None)

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

• core_schema (Union[InvalidSchema, AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, ArgumentsV3Schema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.

• handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

dict[str, Any]

Returns:

A JSON schema, as a Python object.

__init__(**data)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

data (Any)

Return type:

None

__iter__()

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__pretty__(fmt, **kwargs)

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:

Generator[Any, None, None]

Parameters:

• fmt (Callable[[Any], Any])

• kwargs (Any)

classmethod __pydantic_init_subclass__(**kwargs)

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:

**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.

Return type:

None

__repr_name__()

Name of the instance’s class, used in __repr__.

Return type:

str

__repr_recursion__(object)

Returns the string representation of a recursive object.

Return type:

str

Parameters:

object (Any)

__rich_repr__()

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:

Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

classmethod construct(_fields_set=None, **values)

Return type:

Self

Parameters:

• _fields_set (set[str] | None)

• values (Any)

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

• include – Optional set or mapping specifying which fields to include in the copied model.

• exclude – Optional set or mapping specifying which fields to exclude in the copied model.

• update – Optional dictionary of field-value pairs to override field values in the copied model.

• deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Return type:

Dict[str, Any]

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

classmethod from_orm(obj)

Return type:

Self

Parameters:

obj (Any)

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)

Return type:

str

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

• encoder (Callable[[Any], Any] | None)

• models_as_dict (bool)

• dumps_kwargs (Any)

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

• _fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

• values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

!!! abstract “Usage Documentation”

[model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update (Mapping[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#modelmodel_dump)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

• mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to use the field’s alias in the dictionary key if defined.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

dict[str, Any]

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

• indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to serialize using field aliases.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

str

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'aadhar_no': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'branch': FieldInfo(annotation=str, required=True), 'contact_no': FieldInfo(annotation=str, required=True), 'email_id': FieldInfo(annotation=str, required=True), 'image': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'name': FieldInfo(annotation=str, required=True), 'name_as_in_aadhar': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'pesu_id': FieldInfo(annotation=str, required=True), 'program': FieldInfo(annotation=str, required=True), 'section': FieldInfo(annotation=str, required=True), 'semester': FieldInfo(annotation=str, required=True), 'srn': FieldInfo(annotation=str, required=True)}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')

Generates a JSON schema for a model class.

Parameters:

• by_alias (bool) – Whether to use attribute aliases or not.

• ref_template (str) – The reference template.

• schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

• mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Return type:

str

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(context, /)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:

None

Parameters:

context (Any)

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

• force (bool) – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors (bool) – Whether to raise errors, defaults to True.

• _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

• _types_namespace (Mapping[str, Any] | None) – The types namespace, defaults to None.

Return type:

bool | None

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)

Validate a pydantic model instance.

Parameters:

• obj (Any) – The object to validate.

• strict (bool | None) – Whether to enforce types strictly.

• from_attributes (bool | None) – Whether to extract data from object attributes.

• context (Any | None) – Additional context to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data (str | bytes | bytearray) – The JSON data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)

Validate the given object with string data against the Pydantic model.

Parameters:

• obj (Any) – The object containing string data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• path (str | pathlib.Path)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod parse_obj(obj)

Return type:

Self

Parameters:

obj (Any)

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• b (str | bytes)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')

Return type:

Dict[str, Any]

Parameters:

• by_alias (bool)

• ref_template (str)

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)

Return type:

str

Parameters:

• by_alias (bool)

• ref_template (str)

• dumps_kwargs (Any)

classmethod update_forward_refs(**localns)

Return type:

None

Parameters:

localns (Any)

classmethod validate(value)

Return type:

Self

Parameters:

value (Any)

class pesuacademy.models.profile.OtherInformation(**data)

Bases: pydantic.main.BaseModel

Represents other personal information of a user in the PESU Academy system.

Parameters:

• sslc_marks (str)

• puc_marks (str)

• date_of_birth (str)

• blood_group (str)

sslc_marks

Marks obtained in SSLC.

Type:

str

puc_marks

Marks obtained in PUC.

Type:

str

date_of_birth

Date of birth of the user.

Type:

str

blood_group

Blood group of the user.

Type:

str

sslc_marks: str

puc_marks: str

date_of_birth: str

blood_group: str

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

Parameters:

memo (dict[int, Any] | None)

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

• core_schema (Union[InvalidSchema, AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, ArgumentsV3Schema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.

• handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

dict[str, Any]

Returns:

A JSON schema, as a Python object.

__init__(**data)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

data (Any)

Return type:

None

__iter__()

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__pretty__(fmt, **kwargs)

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:

Generator[Any, None, None]

Parameters:

• fmt (Callable[[Any], Any])

• kwargs (Any)

classmethod __pydantic_init_subclass__(**kwargs)

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:

**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.

Return type:

None

__repr_name__()

Name of the instance’s class, used in __repr__.

Return type:

str

__repr_recursion__(object)

Returns the string representation of a recursive object.

Return type:

str

Parameters:

object (Any)

__rich_repr__()

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:

Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

classmethod construct(_fields_set=None, **values)

Return type:

Self

Parameters:

• _fields_set (set[str] | None)

• values (Any)

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

• include – Optional set or mapping specifying which fields to include in the copied model.

• exclude – Optional set or mapping specifying which fields to exclude in the copied model.

• update – Optional dictionary of field-value pairs to override field values in the copied model.

• deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Return type:

Dict[str, Any]

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

classmethod from_orm(obj)

Return type:

Self

Parameters:

obj (Any)

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)

Return type:

str

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

• encoder (Callable[[Any], Any] | None)

• models_as_dict (bool)

• dumps_kwargs (Any)

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

• _fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

• values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

!!! abstract “Usage Documentation”

[model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update (Mapping[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#modelmodel_dump)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

• mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to use the field’s alias in the dictionary key if defined.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

dict[str, Any]

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

• indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to serialize using field aliases.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

str

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'blood_group': FieldInfo(annotation=str, required=True), 'date_of_birth': FieldInfo(annotation=str, required=True), 'puc_marks': FieldInfo(annotation=str, required=True), 'sslc_marks': FieldInfo(annotation=str, required=True)}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')

Generates a JSON schema for a model class.

Parameters:

• by_alias (bool) – Whether to use attribute aliases or not.

• ref_template (str) – The reference template.

• schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

• mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Return type:

str

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(context, /)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:

None

Parameters:

context (Any)

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

• force (bool) – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors (bool) – Whether to raise errors, defaults to True.

• _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

• _types_namespace (Mapping[str, Any] | None) – The types namespace, defaults to None.

Return type:

bool | None

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)

Validate a pydantic model instance.

Parameters:

• obj (Any) – The object to validate.

• strict (bool | None) – Whether to enforce types strictly.

• from_attributes (bool | None) – Whether to extract data from object attributes.

• context (Any | None) – Additional context to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data (str | bytes | bytearray) – The JSON data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)

Validate the given object with string data against the Pydantic model.

Parameters:

• obj (Any) – The object containing string data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• path (str | pathlib.Path)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod parse_obj(obj)

Return type:

Self

Parameters:

obj (Any)

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• b (str | bytes)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')

Return type:

Dict[str, Any]

Parameters:

• by_alias (bool)

• ref_template (str)

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)

Return type:

str

Parameters:

• by_alias (bool)

• ref_template (str)

• dumps_kwargs (Any)

classmethod update_forward_refs(**localns)

Return type:

None

Parameters:

localns (Any)

classmethod validate(value)

Return type:

Self

Parameters:

value (Any)

class pesuacademy.models.profile.QualifyingExamination(**data)

Bases: pydantic.main.BaseModel

Represents details of a qualifying examination in the PESU Academy system.

Parameters:

• exam (str)

• rank (str)

• score (str | None)

exam

The qualifying examination.

Type:

str

rank

Rank obtained in the examination.

Type:

str

score

Score obtained in the examination.

Type:

str

exam: str

rank: str

score: str | None

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

Parameters:

memo (dict[int, Any] | None)

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

• core_schema (Union[InvalidSchema, AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, ArgumentsV3Schema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.

• handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

dict[str, Any]

Returns:

A JSON schema, as a Python object.

__init__(**data)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

data (Any)

Return type:

None

__iter__()

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__pretty__(fmt, **kwargs)

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:

Generator[Any, None, None]

Parameters:

• fmt (Callable[[Any], Any])

• kwargs (Any)

classmethod __pydantic_init_subclass__(**kwargs)

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:

**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.

Return type:

None

__repr_name__()

Name of the instance’s class, used in __repr__.

Return type:

str

__repr_recursion__(object)

Returns the string representation of a recursive object.

Return type:

str

Parameters:

object (Any)

__rich_repr__()

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:

Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

classmethod construct(_fields_set=None, **values)

Return type:

Self

Parameters:

• _fields_set (set[str] | None)

• values (Any)

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

• include – Optional set or mapping specifying which fields to include in the copied model.

• exclude – Optional set or mapping specifying which fields to exclude in the copied model.

• update – Optional dictionary of field-value pairs to override field values in the copied model.

• deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Return type:

Dict[str, Any]

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

classmethod from_orm(obj)

Return type:

Self

Parameters:

obj (Any)

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)

Return type:

str

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

• encoder (Callable[[Any], Any] | None)

• models_as_dict (bool)

• dumps_kwargs (Any)

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

• _fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

• values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

!!! abstract “Usage Documentation”

[model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update (Mapping[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#modelmodel_dump)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

• mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to use the field’s alias in the dictionary key if defined.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

dict[str, Any]

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

• indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to serialize using field aliases.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

str

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'exam': FieldInfo(annotation=str, required=True), 'rank': FieldInfo(annotation=str, required=True), 'score': FieldInfo(annotation=Union[str, NoneType], required=False, default=None)}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')

Generates a JSON schema for a model class.

Parameters:

• by_alias (bool) – Whether to use attribute aliases or not.

• ref_template (str) – The reference template.

• schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

• mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Return type:

str

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(context, /)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:

None

Parameters:

context (Any)

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

• force (bool) – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors (bool) – Whether to raise errors, defaults to True.

• _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

• _types_namespace (Mapping[str, Any] | None) – The types namespace, defaults to None.

Return type:

bool | None

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)

Validate a pydantic model instance.

Parameters:

• obj (Any) – The object to validate.

• strict (bool | None) – Whether to enforce types strictly.

• from_attributes (bool | None) – Whether to extract data from object attributes.

• context (Any | None) – Additional context to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data (str | bytes | bytearray) – The JSON data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)

Validate the given object with string data against the Pydantic model.

Parameters:

• obj (Any) – The object containing string data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• path (str | pathlib.Path)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod parse_obj(obj)

Return type:

Self

Parameters:

obj (Any)

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• b (str | bytes)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')

Return type:

Dict[str, Any]

Parameters:

• by_alias (bool)

• ref_template (str)

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)

Return type:

str

Parameters:

• by_alias (bool)

• ref_template (str)

• dumps_kwargs (Any)

classmethod update_forward_refs(**localns)

Return type:

None

Parameters:

localns (Any)

classmethod validate(value)

Return type:

Self

Parameters:

value (Any)

class pesuacademy.models.profile.ParentDetails(**data)

Bases: pydantic.main.BaseModel

Represents details of a parent in the PESU Academy system.

Parameters:

• name (str)

• mobile (str)

• email (str)

• occupation (str)

• qualification (str)

• designation (str)

• employer (str)

name

Full name of the parent.

Type:

str

mobile

Mobile number of the parent.

Type:

str

email

Email address of the parent.

Type:

str

occupation

Occupation of the parent.

Type:

str

qualification

Qualification of the parent.

Type:

str

designation

Designation of the parent.

Type:

str

employer

Employer of the parent.

Type:

str

name: str

mobile: str

email: str

occupation: str

qualification: str

designation: str

employer: str

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

Parameters:

memo (dict[int, Any] | None)

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

• core_schema (Union[InvalidSchema, AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, ArgumentsV3Schema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.

• handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

dict[str, Any]

Returns:

A JSON schema, as a Python object.

__init__(**data)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

data (Any)

Return type:

None

__iter__()

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__pretty__(fmt, **kwargs)

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:

Generator[Any, None, None]

Parameters:

• fmt (Callable[[Any], Any])

• kwargs (Any)

classmethod __pydantic_init_subclass__(**kwargs)

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:

**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.

Return type:

None

__repr_name__()

Name of the instance’s class, used in __repr__.

Return type:

str

__repr_recursion__(object)

Returns the string representation of a recursive object.

Return type:

str

Parameters:

object (Any)

__rich_repr__()

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:

Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

classmethod construct(_fields_set=None, **values)

Return type:

Self

Parameters:

• _fields_set (set[str] | None)

• values (Any)

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

• include – Optional set or mapping specifying which fields to include in the copied model.

• exclude – Optional set or mapping specifying which fields to exclude in the copied model.

• update – Optional dictionary of field-value pairs to override field values in the copied model.

• deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Return type:

Dict[str, Any]

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

classmethod from_orm(obj)

Return type:

Self

Parameters:

obj (Any)

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)

Return type:

str

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

• encoder (Callable[[Any], Any] | None)

• models_as_dict (bool)

• dumps_kwargs (Any)

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

• _fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

• values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

!!! abstract “Usage Documentation”

[model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update (Mapping[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#modelmodel_dump)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

• mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to use the field’s alias in the dictionary key if defined.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

dict[str, Any]

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

• indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to serialize using field aliases.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

str

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'designation': FieldInfo(annotation=str, required=True), 'email': FieldInfo(annotation=str, required=True), 'employer': FieldInfo(annotation=str, required=True), 'mobile': FieldInfo(annotation=str, required=True), 'name': FieldInfo(annotation=str, required=True), 'occupation': FieldInfo(annotation=str, required=True), 'qualification': FieldInfo(annotation=str, required=True)}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')

Generates a JSON schema for a model class.

Parameters:

• by_alias (bool) – Whether to use attribute aliases or not.

• ref_template (str) – The reference template.

• schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

• mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Return type:

str

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(context, /)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:

None

Parameters:

context (Any)

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

• force (bool) – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors (bool) – Whether to raise errors, defaults to True.

• _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

• _types_namespace (Mapping[str, Any] | None) – The types namespace, defaults to None.

Return type:

bool | None

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)

Validate a pydantic model instance.

Parameters:

• obj (Any) – The object to validate.

• strict (bool | None) – Whether to enforce types strictly.

• from_attributes (bool | None) – Whether to extract data from object attributes.

• context (Any | None) – Additional context to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data (str | bytes | bytearray) – The JSON data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)

Validate the given object with string data against the Pydantic model.

Parameters:

• obj (Any) – The object containing string data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• path (str | pathlib.Path)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod parse_obj(obj)

Return type:

Self

Parameters:

obj (Any)

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• b (str | bytes)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')

Return type:

Dict[str, Any]

Parameters:

• by_alias (bool)

• ref_template (str)

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)

Return type:

str

Parameters:

• by_alias (bool)

• ref_template (str)

• dumps_kwargs (Any)

classmethod update_forward_refs(**localns)

Return type:

None

Parameters:

localns (Any)

classmethod validate(value)

Return type:

Self

Parameters:

value (Any)

class pesuacademy.models.profile.ParentInformation(**data)

Bases: pydantic.main.BaseModel

Represents information about parents in the PESU Academy system.

Parameters:

• father (pesuacademy.models.profile.ParentDetails)

• mother (pesuacademy.models.profile.ParentDetails)

father

Details of the father.

Type:

ParentDetails

mother

Details of the mother.

Type:

ParentDetails

father: ParentDetails

mother: ParentDetails

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

Parameters:

memo (dict[int, Any] | None)

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

• core_schema (Union[InvalidSchema, AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, ArgumentsV3Schema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.

• handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

dict[str, Any]

Returns:

A JSON schema, as a Python object.

__init__(**data)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

data (Any)

Return type:

None

__iter__()

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__pretty__(fmt, **kwargs)

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:

Generator[Any, None, None]

Parameters:

• fmt (Callable[[Any], Any])

• kwargs (Any)

classmethod __pydantic_init_subclass__(**kwargs)

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:

**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.

Return type:

None

__repr_name__()

Name of the instance’s class, used in __repr__.

Return type:

str

__repr_recursion__(object)

Returns the string representation of a recursive object.

Return type:

str

Parameters:

object (Any)

__rich_repr__()

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:

Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

classmethod construct(_fields_set=None, **values)

Return type:

Self

Parameters:

• _fields_set (set[str] | None)

• values (Any)

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

• include – Optional set or mapping specifying which fields to include in the copied model.

• exclude – Optional set or mapping specifying which fields to exclude in the copied model.

• update – Optional dictionary of field-value pairs to override field values in the copied model.

• deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Return type:

Dict[str, Any]

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

classmethod from_orm(obj)

Return type:

Self

Parameters:

obj (Any)

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)

Return type:

str

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

• encoder (Callable[[Any], Any] | None)

• models_as_dict (bool)

• dumps_kwargs (Any)

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

• _fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

• values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

!!! abstract “Usage Documentation”

[model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update (Mapping[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#modelmodel_dump)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

• mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to use the field’s alias in the dictionary key if defined.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

dict[str, Any]

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

• indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to serialize using field aliases.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

str

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'father': FieldInfo(annotation=ParentDetails, required=True), 'mother': FieldInfo(annotation=ParentDetails, required=True)}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')

Generates a JSON schema for a model class.

Parameters:

• by_alias (bool) – Whether to use attribute aliases or not.

• ref_template (str) – The reference template.

• schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

• mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Return type:

str

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(context, /)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:

None

Parameters:

context (Any)

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

• force (bool) – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors (bool) – Whether to raise errors, defaults to True.

• _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

• _types_namespace (Mapping[str, Any] | None) – The types namespace, defaults to None.

Return type:

bool | None

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)

Validate a pydantic model instance.

Parameters:

• obj (Any) – The object to validate.

• strict (bool | None) – Whether to enforce types strictly.

• from_attributes (bool | None) – Whether to extract data from object attributes.

• context (Any | None) – Additional context to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data (str | bytes | bytearray) – The JSON data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)

Validate the given object with string data against the Pydantic model.

Parameters:

• obj (Any) – The object containing string data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• path (str | pathlib.Path)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod parse_obj(obj)

Return type:

Self

Parameters:

obj (Any)

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• b (str | bytes)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')

Return type:

Dict[str, Any]

Parameters:

• by_alias (bool)

• ref_template (str)

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)

Return type:

str

Parameters:

• by_alias (bool)

• ref_template (str)

• dumps_kwargs (Any)

classmethod update_forward_refs(**localns)

Return type:

None

Parameters:

localns (Any)

classmethod validate(value)

Return type:

Self

Parameters:

value (Any)

class pesuacademy.models.profile.AddressDetails(**data)

Bases: pydantic.main.BaseModel

Represents address details in the PESU Academy system.

Parameters:

• present (str)

• permanent (str)

present

Present address of the user.

Type:

str

permanent

Permanent address of the user.

Type:

str

present: str

permanent: str

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

Parameters:

memo (dict[int, Any] | None)

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

• core_schema (Union[InvalidSchema, AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, ArgumentsV3Schema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.

• handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

dict[str, Any]

Returns:

A JSON schema, as a Python object.

__init__(**data)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

data (Any)

Return type:

None

__iter__()

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__pretty__(fmt, **kwargs)

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:

Generator[Any, None, None]

Parameters:

• fmt (Callable[[Any], Any])

• kwargs (Any)

classmethod __pydantic_init_subclass__(**kwargs)

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:

**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.

Return type:

None

__repr_name__()

Name of the instance’s class, used in __repr__.

Return type:

str

__repr_recursion__(object)

Returns the string representation of a recursive object.

Return type:

str

Parameters:

object (Any)

__rich_repr__()

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:

Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

classmethod construct(_fields_set=None, **values)

Return type:

Self

Parameters:

• _fields_set (set[str] | None)

• values (Any)

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

• include – Optional set or mapping specifying which fields to include in the copied model.

• exclude – Optional set or mapping specifying which fields to exclude in the copied model.

• update – Optional dictionary of field-value pairs to override field values in the copied model.

• deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Return type:

Dict[str, Any]

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

classmethod from_orm(obj)

Return type:

Self

Parameters:

obj (Any)

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)

Return type:

str

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

• encoder (Callable[[Any], Any] | None)

• models_as_dict (bool)

• dumps_kwargs (Any)

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

• _fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

• values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

!!! abstract “Usage Documentation”

[model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update (Mapping[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#modelmodel_dump)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

• mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to use the field’s alias in the dictionary key if defined.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

dict[str, Any]

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

• indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to serialize using field aliases.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

str

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'permanent': FieldInfo(annotation=str, required=True), 'present': FieldInfo(annotation=str, required=True)}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')

Generates a JSON schema for a model class.

Parameters:

• by_alias (bool) – Whether to use attribute aliases or not.

• ref_template (str) – The reference template.

• schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

• mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Return type:

str

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(context, /)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:

None

Parameters:

context (Any)

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

• force (bool) – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors (bool) – Whether to raise errors, defaults to True.

• _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

• _types_namespace (Mapping[str, Any] | None) – The types namespace, defaults to None.

Return type:

bool | None

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)

Validate a pydantic model instance.

Parameters:

• obj (Any) – The object to validate.

• strict (bool | None) – Whether to enforce types strictly.

• from_attributes (bool | None) – Whether to extract data from object attributes.

• context (Any | None) – Additional context to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data (str | bytes | bytearray) – The JSON data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)

Validate the given object with string data against the Pydantic model.

Parameters:

• obj (Any) – The object containing string data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• path (str | pathlib.Path)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod parse_obj(obj)

Return type:

Self

Parameters:

obj (Any)

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• b (str | bytes)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')

Return type:

Dict[str, Any]

Parameters:

• by_alias (bool)

• ref_template (str)

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)

Return type:

str

Parameters:

• by_alias (bool)

• ref_template (str)

• dumps_kwargs (Any)

classmethod update_forward_refs(**localns)

Return type:

None

Parameters:

localns (Any)

classmethod validate(value)

Return type:

Self

Parameters:

value (Any)

class pesuacademy.models.profile.Profile(**data)

Bases: pydantic.main.BaseModel

Represents a user’s profile in the PESU Academy system.

Parameters:

• personal (pesuacademy.models.profile.PersonalDetails)

• other_info (pesuacademy.models.profile.OtherInformation)

• qualifying_exam (pesuacademy.models.profile.QualifyingExamination)

• parents (pesuacademy.models.profile.ParentInformation)

• address (pesuacademy.models.profile.AddressDetails)

personal

Personal details of the user.

Type:

PersonalDetails

other_info

Other personal information of the user.

Type:

OtherInformation

qualifying_exam

Details of the qualifying examination.

Type:

QualifyingExamination

parents

Information about the user’s parents.

Type:

ParentInformation

address

Address details of the user.

Type:

AddressDetails

personal: PersonalDetails

other_info: OtherInformation

qualifying_exam: QualifyingExamination

parents: ParentInformation

address: AddressDetails

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

Parameters:

memo (dict[int, Any] | None)

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

• core_schema (Union[InvalidSchema, AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, ArgumentsV3Schema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.

• handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

dict[str, Any]

Returns:

A JSON schema, as a Python object.

__init__(**data)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

data (Any)

Return type:

None

__iter__()

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__pretty__(fmt, **kwargs)

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:

Generator[Any, None, None]

Parameters:

• fmt (Callable[[Any], Any])

• kwargs (Any)

classmethod __pydantic_init_subclass__(**kwargs)

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:

**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.

Return type:

None

__repr_name__()

Name of the instance’s class, used in __repr__.

Return type:

str

__repr_recursion__(object)

Returns the string representation of a recursive object.

Return type:

str

Parameters:

object (Any)

__rich_repr__()

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:

Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

classmethod construct(_fields_set=None, **values)

Return type:

Self

Parameters:

• _fields_set (set[str] | None)

• values (Any)

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

• include – Optional set or mapping specifying which fields to include in the copied model.

• exclude – Optional set or mapping specifying which fields to exclude in the copied model.

• update – Optional dictionary of field-value pairs to override field values in the copied model.

• deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Return type:

Dict[str, Any]

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

classmethod from_orm(obj)

Return type:

Self

Parameters:

obj (Any)

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)

Return type:

str

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

• encoder (Callable[[Any], Any] | None)

• models_as_dict (bool)

• dumps_kwargs (Any)

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

• _fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

• values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

!!! abstract “Usage Documentation”

[model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update (Mapping[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#modelmodel_dump)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

• mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to use the field’s alias in the dictionary key if defined.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

dict[str, Any]

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

• indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to serialize using field aliases.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

str

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'address': FieldInfo(annotation=AddressDetails, required=True), 'other_info': FieldInfo(annotation=OtherInformation, required=True), 'parents': FieldInfo(annotation=ParentInformation, required=True), 'personal': FieldInfo(annotation=PersonalDetails, required=True), 'qualifying_exam': FieldInfo(annotation=QualifyingExamination, required=True)}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')

Generates a JSON schema for a model class.

Parameters:

• by_alias (bool) – Whether to use attribute aliases or not.

• ref_template (str) – The reference template.

• schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

• mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Return type:

str

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(context, /)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:

None

Parameters:

context (Any)

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

• force (bool) – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors (bool) – Whether to raise errors, defaults to True.

• _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

• _types_namespace (Mapping[str, Any] | None) – The types namespace, defaults to None.

Return type:

bool | None

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)

Validate a pydantic model instance.

Parameters:

• obj (Any) – The object to validate.

• strict (bool | None) – Whether to enforce types strictly.

• from_attributes (bool | None) – Whether to extract data from object attributes.

• context (Any | None) – Additional context to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data (str | bytes | bytearray) – The JSON data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)

Validate the given object with string data against the Pydantic model.

Parameters:

• obj (Any) – The object containing string data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• path (str | pathlib.Path)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod parse_obj(obj)

Return type:

Self

Parameters:

obj (Any)

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• b (str | bytes)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')

Return type:

Dict[str, Any]

Parameters:

• by_alias (bool)

• ref_template (str)

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)

Return type:

str

Parameters:

• by_alias (bool)

• ref_template (str)

• dumps_kwargs (Any)

classmethod update_forward_refs(**localns)

Return type:

None

Parameters:

localns (Any)

classmethod validate(value)

Return type:

Self

Parameters:

value (Any)

pesuacademy.models.results module

Model for results in the PESU Academy system.

class pesuacademy.models.results.Assessment(**data)

Bases: pydantic.main.BaseModel

Represents an assessment(e.g., ISA1, MATLAB) in the PESU Academy system.

Parameters:

• name (str)

• marks (str | None)

• total (str | None)

name

Name of the assessment.

Type:

str

marks

Marks obtained in the assessment, if applicable. (e.g., 72)

Type:

Optional[str]

total

Total marks for the assessment, if applicable. (e.g., 100)

Type:

Optional[str]

name: str

marks: str | None

total: str | None

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

Parameters:

memo (dict[int, Any] | None)

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

• core_schema (Union[InvalidSchema, AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, ArgumentsV3Schema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.

• handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

dict[str, Any]

Returns:

A JSON schema, as a Python object.

__init__(**data)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

data (Any)

Return type:

None

__iter__()

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__pretty__(fmt, **kwargs)

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:

Generator[Any, None, None]

Parameters:

• fmt (Callable[[Any], Any])

• kwargs (Any)

classmethod __pydantic_init_subclass__(**kwargs)

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:

**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.

Return type:

None

__repr_name__()

Name of the instance’s class, used in __repr__.

Return type:

str

__repr_recursion__(object)

Returns the string representation of a recursive object.

Return type:

str

Parameters:

object (Any)

__rich_repr__()

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:

Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

classmethod construct(_fields_set=None, **values)

Return type:

Self

Parameters:

• _fields_set (set[str] | None)

• values (Any)

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

• include – Optional set or mapping specifying which fields to include in the copied model.

• exclude – Optional set or mapping specifying which fields to exclude in the copied model.

• update – Optional dictionary of field-value pairs to override field values in the copied model.

• deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Return type:

Dict[str, Any]

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

classmethod from_orm(obj)

Return type:

Self

Parameters:

obj (Any)

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)

Return type:

str

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

• encoder (Callable[[Any], Any] | None)

• models_as_dict (bool)

• dumps_kwargs (Any)

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

• _fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

• values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

!!! abstract “Usage Documentation”

[model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update (Mapping[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#modelmodel_dump)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

• mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to use the field’s alias in the dictionary key if defined.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

dict[str, Any]

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

• indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to serialize using field aliases.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

str

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'marks': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'name': FieldInfo(annotation=str, required=True), 'total': FieldInfo(annotation=Union[str, NoneType], required=False, default=None)}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')

Generates a JSON schema for a model class.

Parameters:

• by_alias (bool) – Whether to use attribute aliases or not.

• ref_template (str) – The reference template.

• schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

• mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Return type:

str

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(context, /)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:

None

Parameters:

context (Any)

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

• force (bool) – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors (bool) – Whether to raise errors, defaults to True.

• _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

• _types_namespace (Mapping[str, Any] | None) – The types namespace, defaults to None.

Return type:

bool | None

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)

Validate a pydantic model instance.

Parameters:

• obj (Any) – The object to validate.

• strict (bool | None) – Whether to enforce types strictly.

• from_attributes (bool | None) – Whether to extract data from object attributes.

• context (Any | None) – Additional context to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data (str | bytes | bytearray) – The JSON data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)

Validate the given object with string data against the Pydantic model.

Parameters:

• obj (Any) – The object containing string data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• path (str | pathlib.Path)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod parse_obj(obj)

Return type:

Self

Parameters:

obj (Any)

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• b (str | bytes)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')

Return type:

Dict[str, Any]

Parameters:

• by_alias (bool)

• ref_template (str)

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)

Return type:

str

Parameters:

• by_alias (bool)

• ref_template (str)

• dumps_kwargs (Any)

classmethod update_forward_refs(**localns)

Return type:

None

Parameters:

localns (Any)

classmethod validate(value)

Return type:

Self

Parameters:

value (Any)

class pesuacademy.models.results.Credits(**data)

Bases: pydantic.main.BaseModel

Represents credits information for a course in the PESU Academy system.

Parameters:

• earned (str)

• total (str)

earned

Credits earned for the course.

Type:

str

total

Total credits available for the course.

Type:

str

earned: str

total: str

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

Parameters:

memo (dict[int, Any] | None)

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

• core_schema (Union[InvalidSchema, AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, ArgumentsV3Schema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.

• handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

dict[str, Any]

Returns:

A JSON schema, as a Python object.

__init__(**data)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

data (Any)

Return type:

None

__iter__()

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__pretty__(fmt, **kwargs)

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:

Generator[Any, None, None]

Parameters:

• fmt (Callable[[Any], Any])

• kwargs (Any)

classmethod __pydantic_init_subclass__(**kwargs)

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:

**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.

Return type:

None

__repr_name__()

Name of the instance’s class, used in __repr__.

Return type:

str

__repr_recursion__(object)

Returns the string representation of a recursive object.

Return type:

str

Parameters:

object (Any)

__rich_repr__()

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:

Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

classmethod construct(_fields_set=None, **values)

Return type:

Self

Parameters:

• _fields_set (set[str] | None)

• values (Any)

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

• include – Optional set or mapping specifying which fields to include in the copied model.

• exclude – Optional set or mapping specifying which fields to exclude in the copied model.

• update – Optional dictionary of field-value pairs to override field values in the copied model.

• deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Return type:

Dict[str, Any]

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

classmethod from_orm(obj)

Return type:

Self

Parameters:

obj (Any)

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)

Return type:

str

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

• encoder (Callable[[Any], Any] | None)

• models_as_dict (bool)

• dumps_kwargs (Any)

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

• _fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

• values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

!!! abstract “Usage Documentation”

[model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update (Mapping[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#modelmodel_dump)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

• mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to use the field’s alias in the dictionary key if defined.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

dict[str, Any]

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

• indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to serialize using field aliases.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

str

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'earned': FieldInfo(annotation=str, required=True), 'total': FieldInfo(annotation=str, required=True)}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')

Generates a JSON schema for a model class.

Parameters:

• by_alias (bool) – Whether to use attribute aliases or not.

• ref_template (str) – The reference template.

• schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

• mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Return type:

str

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(context, /)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:

None

Parameters:

context (Any)

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

• force (bool) – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors (bool) – Whether to raise errors, defaults to True.

• _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

• _types_namespace (Mapping[str, Any] | None) – The types namespace, defaults to None.

Return type:

bool | None

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)

Validate a pydantic model instance.

Parameters:

• obj (Any) – The object to validate.

• strict (bool | None) – Whether to enforce types strictly.

• from_attributes (bool | None) – Whether to extract data from object attributes.

• context (Any | None) – Additional context to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data (str | bytes | bytearray) – The JSON data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)

Validate the given object with string data against the Pydantic model.

Parameters:

• obj (Any) – The object containing string data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• path (str | pathlib.Path)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod parse_obj(obj)

Return type:

Self

Parameters:

obj (Any)

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• b (str | bytes)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')

Return type:

Dict[str, Any]

Parameters:

• by_alias (bool)

• ref_template (str)

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)

Return type:

str

Parameters:

• by_alias (bool)

• ref_template (str)

• dumps_kwargs (Any)

classmethod update_forward_refs(**localns)

Return type:

None

Parameters:

localns (Any)

classmethod validate(value)

Return type:

Self

Parameters:

value (Any)

class pesuacademy.models.results.CourseResult(**data)

Bases: pydantic.main.BaseModel

Represents the result of a course in the PESU Academy system.

Parameters:

• code (str)

• title (str)

• credits (pesuacademy.models.results.Credits | None)

• assessments (list[pesuacademy.models.results.Assessment])

code

Unique identifier for the course.

Type:

str

title

Title of the course.

Type:

str

credits

Credits information for the course, if available.

Type:

Optional[Credits]

assessments

List of assessments associated with the course.

Type:

List[Assessment]

code: str

title: str

credits: Credits | None

assessments: list[Assessment]

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

Parameters:

memo (dict[int, Any] | None)

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

• core_schema (Union[InvalidSchema, AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, ArgumentsV3Schema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.

• handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

dict[str, Any]

Returns:

A JSON schema, as a Python object.

__init__(**data)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

data (Any)

Return type:

None

__iter__()

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__pretty__(fmt, **kwargs)

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:

Generator[Any, None, None]

Parameters:

• fmt (Callable[[Any], Any])

• kwargs (Any)

classmethod __pydantic_init_subclass__(**kwargs)

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:

**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.

Return type:

None

__repr_name__()

Name of the instance’s class, used in __repr__.

Return type:

str

__repr_recursion__(object)

Returns the string representation of a recursive object.

Return type:

str

Parameters:

object (Any)

__rich_repr__()

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:

Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

classmethod construct(_fields_set=None, **values)

Return type:

Self

Parameters:

• _fields_set (set[str] | None)

• values (Any)

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

• include – Optional set or mapping specifying which fields to include in the copied model.

• exclude – Optional set or mapping specifying which fields to exclude in the copied model.

• update – Optional dictionary of field-value pairs to override field values in the copied model.

• deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Return type:

Dict[str, Any]

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

classmethod from_orm(obj)

Return type:

Self

Parameters:

obj (Any)

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)

Return type:

str

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

• encoder (Callable[[Any], Any] | None)

• models_as_dict (bool)

• dumps_kwargs (Any)

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

• _fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

• values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

!!! abstract “Usage Documentation”

[model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update (Mapping[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#modelmodel_dump)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

• mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to use the field’s alias in the dictionary key if defined.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

dict[str, Any]

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

• indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to serialize using field aliases.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

str

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'assessments': FieldInfo(annotation=list[Assessment], required=True), 'code': FieldInfo(annotation=str, required=True), 'credits': FieldInfo(annotation=Union[Credits, NoneType], required=False, default=None), 'title': FieldInfo(annotation=str, required=True)}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')

Generates a JSON schema for a model class.

Parameters:

• by_alias (bool) – Whether to use attribute aliases or not.

• ref_template (str) – The reference template.

• schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

• mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Return type:

str

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(context, /)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:

None

Parameters:

context (Any)

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

• force (bool) – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors (bool) – Whether to raise errors, defaults to True.

• _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

• _types_namespace (Mapping[str, Any] | None) – The types namespace, defaults to None.

Return type:

bool | None

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)

Validate a pydantic model instance.

Parameters:

• obj (Any) – The object to validate.

• strict (bool | None) – Whether to enforce types strictly.

• from_attributes (bool | None) – Whether to extract data from object attributes.

• context (Any | None) – Additional context to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data (str | bytes | bytearray) – The JSON data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)

Validate the given object with string data against the Pydantic model.

Parameters:

• obj (Any) – The object containing string data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• path (str | pathlib.Path)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod parse_obj(obj)

Return type:

Self

Parameters:

obj (Any)

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• b (str | bytes)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')

Return type:

Dict[str, Any]

Parameters:

• by_alias (bool)

• ref_template (str)

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)

Return type:

str

Parameters:

• by_alias (bool)

• ref_template (str)

• dumps_kwargs (Any)

classmethod update_forward_refs(**localns)

Return type:

None

Parameters:

localns (Any)

classmethod validate(value)

Return type:

Self

Parameters:

value (Any)

class pesuacademy.models.results.SemesterResult(**data)

Bases: pydantic.main.BaseModel

Represents the result of a semester in the PESU Academy system.

Parameters:

• sgpa (str)

• credits (pesuacademy.models.results.Credits | None)

• courses (list[pesuacademy.models.results.CourseResult])

semester

Semester number.

Type:

str

sgpa

Semester Grade Point Average.

Type:

str

credits

Credits information for the semester, if available.

Type:

Optional[Credits]

courses

List of course results for the semester.

Type:

List[CourseResult]

sgpa: str

credits: Credits | None

courses: list[CourseResult]

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

Parameters:

memo (dict[int, Any] | None)

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

• core_schema (Union[InvalidSchema, AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, ArgumentsV3Schema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.

• handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

dict[str, Any]

Returns:

A JSON schema, as a Python object.

__init__(**data)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

data (Any)

Return type:

None

__iter__()

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__pretty__(fmt, **kwargs)

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:

Generator[Any, None, None]

Parameters:

• fmt (Callable[[Any], Any])

• kwargs (Any)

classmethod __pydantic_init_subclass__(**kwargs)

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:

**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.

Return type:

None

__repr_name__()

Name of the instance’s class, used in __repr__.

Return type:

str

__repr_recursion__(object)

Returns the string representation of a recursive object.

Return type:

str

Parameters:

object (Any)

__rich_repr__()

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:

Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

classmethod construct(_fields_set=None, **values)

Return type:

Self

Parameters:

• _fields_set (set[str] | None)

• values (Any)

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

• include – Optional set or mapping specifying which fields to include in the copied model.

• exclude – Optional set or mapping specifying which fields to exclude in the copied model.

• update – Optional dictionary of field-value pairs to override field values in the copied model.

• deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Return type:

Dict[str, Any]

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

classmethod from_orm(obj)

Return type:

Self

Parameters:

obj (Any)

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)

Return type:

str

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

• encoder (Callable[[Any], Any] | None)

• models_as_dict (bool)

• dumps_kwargs (Any)

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

• _fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

• values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

!!! abstract “Usage Documentation”

[model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update (Mapping[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#modelmodel_dump)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

• mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to use the field’s alias in the dictionary key if defined.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

dict[str, Any]

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

• indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to serialize using field aliases.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

str

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'courses': FieldInfo(annotation=list[CourseResult], required=True), 'credits': FieldInfo(annotation=Union[Credits, NoneType], required=False, default=None), 'sgpa': FieldInfo(annotation=str, required=True)}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')

Generates a JSON schema for a model class.

Parameters:

• by_alias (bool) – Whether to use attribute aliases or not.

• ref_template (str) – The reference template.

• schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

• mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Return type:

str

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(context, /)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:

None

Parameters:

context (Any)

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

• force (bool) – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors (bool) – Whether to raise errors, defaults to True.

• _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

• _types_namespace (Mapping[str, Any] | None) – The types namespace, defaults to None.

Return type:

bool | None

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)

Validate a pydantic model instance.

Parameters:

• obj (Any) – The object to validate.

• strict (bool | None) – Whether to enforce types strictly.

• from_attributes (bool | None) – Whether to extract data from object attributes.

• context (Any | None) – Additional context to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data (str | bytes | bytearray) – The JSON data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)

Validate the given object with string data against the Pydantic model.

Parameters:

• obj (Any) – The object containing string data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• path (str | pathlib.Path)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod parse_obj(obj)

Return type:

Self

Parameters:

obj (Any)

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• b (str | bytes)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')

Return type:

Dict[str, Any]

Parameters:

• by_alias (bool)

• ref_template (str)

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)

Return type:

str

Parameters:

• by_alias (bool)

• ref_template (str)

• dumps_kwargs (Any)

classmethod update_forward_refs(**localns)

Return type:

None

Parameters:

localns (Any)

classmethod validate(value)

Return type:

Self

Parameters:

value (Any)

pesuacademy.models.seating_information module

Model for seating information in the PESU Academy system.

class pesuacademy.models.seating_information.SeatingInformation(**data)

Bases: pydantic.main.BaseModel

Represents seating information in the PESU Academy system.

Parameters:

• name (str)

• course_code (str)

• date (str)

• time (str)

• terminal (str)

• block (str)

name

Name of the course/assessment.

Type:

str

course_code

Code of the course for which seating information is provided.

Type:

str

date

Date of the examination or event.

Type:

str

time

Time of the examination or event.

Type:

str

terminal

Terminal or location where the student is seated.

Type:

str

block

Block or section of the seating arrangement.

Type:

str

name: str

course_code: str

date: str

time: str

terminal: str

block: str

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

Parameters:

memo (dict[int, Any] | None)

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

• core_schema (Union[InvalidSchema, AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, ArgumentsV3Schema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.

• handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

dict[str, Any]

Returns:

A JSON schema, as a Python object.

__init__(**data)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

data (Any)

Return type:

None

__iter__()

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__pretty__(fmt, **kwargs)

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:

Generator[Any, None, None]

Parameters:

• fmt (Callable[[Any], Any])

• kwargs (Any)

classmethod __pydantic_init_subclass__(**kwargs)

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:

**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.

Return type:

None

__repr_name__()

Name of the instance’s class, used in __repr__.

Return type:

str

__repr_recursion__(object)

Returns the string representation of a recursive object.

Return type:

str

Parameters:

object (Any)

__rich_repr__()

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:

Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

classmethod construct(_fields_set=None, **values)

Return type:

Self

Parameters:

• _fields_set (set[str] | None)

• values (Any)

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

• include – Optional set or mapping specifying which fields to include in the copied model.

• exclude – Optional set or mapping specifying which fields to exclude in the copied model.

• update – Optional dictionary of field-value pairs to override field values in the copied model.

• deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Return type:

Dict[str, Any]

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

classmethod from_orm(obj)

Return type:

Self

Parameters:

obj (Any)

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)

Return type:

str

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

• encoder (Callable[[Any], Any] | None)

• models_as_dict (bool)

• dumps_kwargs (Any)

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

• _fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

• values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

!!! abstract “Usage Documentation”

[model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update (Mapping[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#modelmodel_dump)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

• mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to use the field’s alias in the dictionary key if defined.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

dict[str, Any]

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

• indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to serialize using field aliases.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

str

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'block': FieldInfo(annotation=str, required=True), 'course_code': FieldInfo(annotation=str, required=True), 'date': FieldInfo(annotation=str, required=True), 'name': FieldInfo(annotation=str, required=True), 'terminal': FieldInfo(annotation=str, required=True), 'time': FieldInfo(annotation=str, required=True)}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')

Generates a JSON schema for a model class.

Parameters:

• by_alias (bool) – Whether to use attribute aliases or not.

• ref_template (str) – The reference template.

• schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

• mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Return type:

str

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(context, /)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:

None

Parameters:

context (Any)

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

• force (bool) – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors (bool) – Whether to raise errors, defaults to True.

• _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

• _types_namespace (Mapping[str, Any] | None) – The types namespace, defaults to None.

Return type:

bool | None

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)

Validate a pydantic model instance.

Parameters:

• obj (Any) – The object to validate.

• strict (bool | None) – Whether to enforce types strictly.

• from_attributes (bool | None) – Whether to extract data from object attributes.

• context (Any | None) – Additional context to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data (str | bytes | bytearray) – The JSON data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)

Validate the given object with string data against the Pydantic model.

Parameters:

• obj (Any) – The object containing string data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• path (str | pathlib.Path)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod parse_obj(obj)

Return type:

Self

Parameters:

obj (Any)

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• b (str | bytes)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')

Return type:

Dict[str, Any]

Parameters:

• by_alias (bool)

• ref_template (str)

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)

Return type:

str

Parameters:

• by_alias (bool)

• ref_template (str)

• dumps_kwargs (Any)

classmethod update_forward_refs(**localns)

Return type:

None

Parameters:

localns (Any)

classmethod validate(value)

Return type:

Self

Parameters:

value (Any)

Module contents

This module initializes the models for the PESU Academy package.

class pesuacademy.models.Announcement(**data)

Bases: pydantic.main.BaseModel

Represents an announcement in the PESU Academy system.

Parameters:

• title (str)

• date (datetime.date)

• content (str)

• attachments (list[str] | None)

title

The title of the announcement.

Type:

str

date

The date of the announcement.

Type:

datetime.date

content

The content of the announcement.

Type:

str

attachments

Optional list of attachment links related to the announcement.

Type:

Optional[List[str]]

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

Parameters:

memo (dict[int, Any] | None)

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

• core_schema (Union[InvalidSchema, AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, ArgumentsV3Schema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.

• handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

dict[str, Any]

Returns:

A JSON schema, as a Python object.

__init__(**data)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

data (Any)

Return type:

None

__iter__()

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__pretty__(fmt, **kwargs)

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:

Generator[Any, None, None]

Parameters:

• fmt (Callable[[Any], Any])

• kwargs (Any)

classmethod __pydantic_init_subclass__(**kwargs)

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:

**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.

Return type:

None

__repr_name__()

Name of the instance’s class, used in __repr__.

Return type:

str

__repr_recursion__(object)

Returns the string representation of a recursive object.

Return type:

str

Parameters:

object (Any)

__rich_repr__()

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:

Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

classmethod construct(_fields_set=None, **values)

Return type:

Self

Parameters:

• _fields_set (set[str] | None)

• values (Any)

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

• include – Optional set or mapping specifying which fields to include in the copied model.

• exclude – Optional set or mapping specifying which fields to exclude in the copied model.

• update – Optional dictionary of field-value pairs to override field values in the copied model.

• deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Return type:

Dict[str, Any]

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

classmethod from_orm(obj)

Return type:

Self

Parameters:

obj (Any)

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)

Return type:

str

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

• encoder (Callable[[Any], Any] | None)

• models_as_dict (bool)

• dumps_kwargs (Any)

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

• _fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

• values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

!!! abstract “Usage Documentation”

[model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update (Mapping[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#modelmodel_dump)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

• mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to use the field’s alias in the dictionary key if defined.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

dict[str, Any]

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

• indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to serialize using field aliases.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

str

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'attachments': FieldInfo(annotation=Union[list[str], NoneType], required=False, default=None), 'content': FieldInfo(annotation=str, required=True), 'date': FieldInfo(annotation=date, required=True), 'title': FieldInfo(annotation=str, required=True)}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')

Generates a JSON schema for a model class.

Parameters:

• by_alias (bool) – Whether to use attribute aliases or not.

• ref_template (str) – The reference template.

• schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

• mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Return type:

str

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(context, /)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:

None

Parameters:

context (Any)

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

• force (bool) – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors (bool) – Whether to raise errors, defaults to True.

• _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

• _types_namespace (Mapping[str, Any] | None) – The types namespace, defaults to None.

Return type:

bool | None

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)

Validate a pydantic model instance.

Parameters:

• obj (Any) – The object to validate.

• strict (bool | None) – Whether to enforce types strictly.

• from_attributes (bool | None) – Whether to extract data from object attributes.

• context (Any | None) – Additional context to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data (str | bytes | bytearray) – The JSON data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)

Validate the given object with string data against the Pydantic model.

Parameters:

• obj (Any) – The object containing string data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• path (str | pathlib.Path)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod parse_obj(obj)

Return type:

Self

Parameters:

obj (Any)

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• b (str | bytes)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')

Return type:

Dict[str, Any]

Parameters:

• by_alias (bool)

• ref_template (str)

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)

Return type:

str

Parameters:

• by_alias (bool)

• ref_template (str)

• dumps_kwargs (Any)

classmethod update_forward_refs(**localns)

Return type:

None

Parameters:

localns (Any)

classmethod validate(value)

Return type:

Self

Parameters:

value (Any)

title: str

date: date

content: str

attachments: list[str] | None

class pesuacademy.models.Attendance(**data)

Bases: pydantic.main.BaseModel

Represents attendance information for a course.

Parameters:

• attended (int | None)

• total (int | None)

• percentage (float | None)

attended

Number of classes attended by the student.

Type:

Optional[int]

total

Total number of classes for the course.

Type:

Optional[int]

percentage

Attendance percentage calculated from attended and total classes.

Type:

Optional[float]

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

Parameters:

memo (dict[int, Any] | None)

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

• core_schema (Union[InvalidSchema, AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, ArgumentsV3Schema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.

• handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

dict[str, Any]

Returns:

A JSON schema, as a Python object.

__init__(**data)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

data (Any)

Return type:

None

__iter__()

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__pretty__(fmt, **kwargs)

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:

Generator[Any, None, None]

Parameters:

• fmt (Callable[[Any], Any])

• kwargs (Any)

classmethod __pydantic_init_subclass__(**kwargs)

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:

**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.

Return type:

None

__repr_name__()

Name of the instance’s class, used in __repr__.

Return type:

str

__repr_recursion__(object)

Returns the string representation of a recursive object.

Return type:

str

Parameters:

object (Any)

__rich_repr__()

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:

Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

classmethod construct(_fields_set=None, **values)

Return type:

Self

Parameters:

• _fields_set (set[str] | None)

• values (Any)

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

• include – Optional set or mapping specifying which fields to include in the copied model.

• exclude – Optional set or mapping specifying which fields to exclude in the copied model.

• update – Optional dictionary of field-value pairs to override field values in the copied model.

• deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Return type:

Dict[str, Any]

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

classmethod from_orm(obj)

Return type:

Self

Parameters:

obj (Any)

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)

Return type:

str

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

• encoder (Callable[[Any], Any] | None)

• models_as_dict (bool)

• dumps_kwargs (Any)

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

• _fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

• values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

!!! abstract “Usage Documentation”

[model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update (Mapping[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#modelmodel_dump)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

• mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to use the field’s alias in the dictionary key if defined.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

dict[str, Any]

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

• indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to serialize using field aliases.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

str

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'attended': FieldInfo(annotation=Union[int, NoneType], required=False, default=None), 'percentage': FieldInfo(annotation=Union[float, NoneType], required=False, default=None), 'total': FieldInfo(annotation=Union[int, NoneType], required=False, default=None)}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')

Generates a JSON schema for a model class.

Parameters:

• by_alias (bool) – Whether to use attribute aliases or not.

• ref_template (str) – The reference template.

• schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

• mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Return type:

str

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(context, /)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:

None

Parameters:

context (Any)

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

• force (bool) – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors (bool) – Whether to raise errors, defaults to True.

• _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

• _types_namespace (Mapping[str, Any] | None) – The types namespace, defaults to None.

Return type:

bool | None

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)

Validate a pydantic model instance.

Parameters:

• obj (Any) – The object to validate.

• strict (bool | None) – Whether to enforce types strictly.

• from_attributes (bool | None) – Whether to extract data from object attributes.

• context (Any | None) – Additional context to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data (str | bytes | bytearray) – The JSON data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)

Validate the given object with string data against the Pydantic model.

Parameters:

• obj (Any) – The object containing string data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• path (str | pathlib.Path)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod parse_obj(obj)

Return type:

Self

Parameters:

obj (Any)

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• b (str | bytes)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')

Return type:

Dict[str, Any]

Parameters:

• by_alias (bool)

• ref_template (str)

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)

Return type:

str

Parameters:

• by_alias (bool)

• ref_template (str)

• dumps_kwargs (Any)

classmethod update_forward_refs(**localns)

Return type:

None

Parameters:

localns (Any)

classmethod validate(value)

Return type:

Self

Parameters:

value (Any)

attended: int | None

total: int | None

percentage: float | None

class pesuacademy.models.Course(**data)

Bases: pydantic.main.BaseModel

Represents a course in the PESU Academy system.

Parameters:

• code (str)

• title (str)

• type (str | None)

• status (str | None)

• attendance (pesuacademy.models.course.Attendance | None)

• id (str | None)

code

Unique identifier for the course.

Type:

str

title

Title of the course.

Type:

str

type

Type of the course (e.g., FC, CC).

Type:

Optional[str]

status

Current status of the course (e.g., Enrolled).

Type:

Optional[str]

attendance

Attendance information for the course.

Type:

Optional[Attendance]

id

Unique identifier for the course instance.

Type:

Optional[str]

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

Parameters:

memo (dict[int, Any] | None)

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

• core_schema (Union[InvalidSchema, AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, ArgumentsV3Schema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.

• handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

dict[str, Any]

Returns:

A JSON schema, as a Python object.

__init__(**data)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

data (Any)

Return type:

None

__iter__()

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__pretty__(fmt, **kwargs)

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:

Generator[Any, None, None]

Parameters:

• fmt (Callable[[Any], Any])

• kwargs (Any)

classmethod __pydantic_init_subclass__(**kwargs)

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:

**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.

Return type:

None

__repr_name__()

Name of the instance’s class, used in __repr__.

Return type:

str

__repr_recursion__(object)

Returns the string representation of a recursive object.

Return type:

str

Parameters:

object (Any)

__rich_repr__()

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:

Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

classmethod construct(_fields_set=None, **values)

Return type:

Self

Parameters:

• _fields_set (set[str] | None)

• values (Any)

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

• include – Optional set or mapping specifying which fields to include in the copied model.

• exclude – Optional set or mapping specifying which fields to exclude in the copied model.

• update – Optional dictionary of field-value pairs to override field values in the copied model.

• deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Return type:

Dict[str, Any]

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

classmethod from_orm(obj)

Return type:

Self

Parameters:

obj (Any)

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)

Return type:

str

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

• encoder (Callable[[Any], Any] | None)

• models_as_dict (bool)

• dumps_kwargs (Any)

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

• _fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

• values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

!!! abstract “Usage Documentation”

[model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update (Mapping[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#modelmodel_dump)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

• mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to use the field’s alias in the dictionary key if defined.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

dict[str, Any]

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

• indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to serialize using field aliases.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

str

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'attendance': FieldInfo(annotation=Union[Attendance, NoneType], required=False, default=None), 'code': FieldInfo(annotation=str, required=True), 'id': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'status': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'title': FieldInfo(annotation=str, required=True), 'type': FieldInfo(annotation=Union[str, NoneType], required=False, default=None)}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')

Generates a JSON schema for a model class.

Parameters:

• by_alias (bool) – Whether to use attribute aliases or not.

• ref_template (str) – The reference template.

• schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

• mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Return type:

str

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(context, /)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:

None

Parameters:

context (Any)

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

• force (bool) – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors (bool) – Whether to raise errors, defaults to True.

• _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

• _types_namespace (Mapping[str, Any] | None) – The types namespace, defaults to None.

Return type:

bool | None

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)

Validate a pydantic model instance.

Parameters:

• obj (Any) – The object to validate.

• strict (bool | None) – Whether to enforce types strictly.

• from_attributes (bool | None) – Whether to extract data from object attributes.

• context (Any | None) – Additional context to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data (str | bytes | bytearray) – The JSON data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)

Validate the given object with string data against the Pydantic model.

Parameters:

• obj (Any) – The object containing string data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• path (str | pathlib.Path)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod parse_obj(obj)

Return type:

Self

Parameters:

obj (Any)

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• b (str | bytes)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')

Return type:

Dict[str, Any]

Parameters:

• by_alias (bool)

• ref_template (str)

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)

Return type:

str

Parameters:

• by_alias (bool)

• ref_template (str)

• dumps_kwargs (Any)

classmethod update_forward_refs(**localns)

Return type:

None

Parameters:

localns (Any)

classmethod validate(value)

Return type:

Self

Parameters:

value (Any)

code: str

title: str

type: str | None

status: str | None

attendance: Attendance | None

id: str | None

class pesuacademy.models.MaterialLink(**data)

Bases: pydantic.main.BaseModel

Represents a link to a material in the PESU Academy system.

Parameters:

• title (str)

• url (str)

• is_pdf (bool)

title

The title of the material link.

Type:

str

url

The URL of the material.

Type:

str

is_pdf

Indicates if the link points to a PDF document.

Type:

bool

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

Parameters:

memo (dict[int, Any] | None)

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

• core_schema (Union[InvalidSchema, AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, ArgumentsV3Schema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.

• handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

dict[str, Any]

Returns:

A JSON schema, as a Python object.

__init__(**data)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

data (Any)

Return type:

None

__iter__()

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__pretty__(fmt, **kwargs)

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:

Generator[Any, None, None]

Parameters:

• fmt (Callable[[Any], Any])

• kwargs (Any)

classmethod __pydantic_init_subclass__(**kwargs)

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:

**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.

Return type:

None

__repr_name__()

Name of the instance’s class, used in __repr__.

Return type:

str

__repr_recursion__(object)

Returns the string representation of a recursive object.

Return type:

str

Parameters:

object (Any)

__rich_repr__()

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:

Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

classmethod construct(_fields_set=None, **values)

Return type:

Self

Parameters:

• _fields_set (set[str] | None)

• values (Any)

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

• include – Optional set or mapping specifying which fields to include in the copied model.

• exclude – Optional set or mapping specifying which fields to exclude in the copied model.

• update – Optional dictionary of field-value pairs to override field values in the copied model.

• deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Return type:

Dict[str, Any]

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

classmethod from_orm(obj)

Return type:

Self

Parameters:

obj (Any)

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)

Return type:

str

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

• encoder (Callable[[Any], Any] | None)

• models_as_dict (bool)

• dumps_kwargs (Any)

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

• _fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

• values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

!!! abstract “Usage Documentation”

[model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update (Mapping[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#modelmodel_dump)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

• mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to use the field’s alias in the dictionary key if defined.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

dict[str, Any]

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

• indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to serialize using field aliases.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

str

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'is_pdf': FieldInfo(annotation=bool, required=True), 'title': FieldInfo(annotation=str, required=True), 'url': FieldInfo(annotation=str, required=True)}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')

Generates a JSON schema for a model class.

Parameters:

• by_alias (bool) – Whether to use attribute aliases or not.

• ref_template (str) – The reference template.

• schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

• mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Return type:

str

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(context, /)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:

None

Parameters:

context (Any)

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

• force (bool) – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors (bool) – Whether to raise errors, defaults to True.

• _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

• _types_namespace (Mapping[str, Any] | None) – The types namespace, defaults to None.

Return type:

bool | None

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)

Validate a pydantic model instance.

Parameters:

• obj (Any) – The object to validate.

• strict (bool | None) – Whether to enforce types strictly.

• from_attributes (bool | None) – Whether to extract data from object attributes.

• context (Any | None) – Additional context to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data (str | bytes | bytearray) – The JSON data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)

Validate the given object with string data against the Pydantic model.

Parameters:

• obj (Any) – The object containing string data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• path (str | pathlib.Path)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod parse_obj(obj)

Return type:

Self

Parameters:

obj (Any)

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• b (str | bytes)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')

Return type:

Dict[str, Any]

Parameters:

• by_alias (bool)

• ref_template (str)

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)

Return type:

str

Parameters:

• by_alias (bool)

• ref_template (str)

• dumps_kwargs (Any)

classmethod update_forward_refs(**localns)

Return type:

None

Parameters:

localns (Any)

classmethod validate(value)

Return type:

Self

Parameters:

value (Any)

title: str

url: str

is_pdf: bool

class pesuacademy.models.Profile(**data)

Bases: pydantic.main.BaseModel

Represents a user’s profile in the PESU Academy system.

Parameters:

• personal (pesuacademy.models.profile.PersonalDetails)

• other_info (pesuacademy.models.profile.OtherInformation)

• qualifying_exam (pesuacademy.models.profile.QualifyingExamination)

• parents (pesuacademy.models.profile.ParentInformation)

• address (pesuacademy.models.profile.AddressDetails)

personal

Personal details of the user.

Type:

PersonalDetails

other_info

Other personal information of the user.

Type:

OtherInformation

qualifying_exam

Details of the qualifying examination.

Type:

QualifyingExamination

parents

Information about the user’s parents.

Type:

ParentInformation

address

Address details of the user.

Type:

AddressDetails

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

Parameters:

memo (dict[int, Any] | None)

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

• core_schema (Union[InvalidSchema, AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, ArgumentsV3Schema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.

• handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

dict[str, Any]

Returns:

A JSON schema, as a Python object.

__init__(**data)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

data (Any)

Return type:

None

__iter__()

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__pretty__(fmt, **kwargs)

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:

Generator[Any, None, None]

Parameters:

• fmt (Callable[[Any], Any])

• kwargs (Any)

classmethod __pydantic_init_subclass__(**kwargs)

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:

**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.

Return type:

None

__repr_name__()

Name of the instance’s class, used in __repr__.

Return type:

str

__repr_recursion__(object)

Returns the string representation of a recursive object.

Return type:

str

Parameters:

object (Any)

__rich_repr__()

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:

Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

classmethod construct(_fields_set=None, **values)

Return type:

Self

Parameters:

• _fields_set (set[str] | None)

• values (Any)

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

• include – Optional set or mapping specifying which fields to include in the copied model.

• exclude – Optional set or mapping specifying which fields to exclude in the copied model.

• update – Optional dictionary of field-value pairs to override field values in the copied model.

• deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Return type:

Dict[str, Any]

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

classmethod from_orm(obj)

Return type:

Self

Parameters:

obj (Any)

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)

Return type:

str

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

• encoder (Callable[[Any], Any] | None)

• models_as_dict (bool)

• dumps_kwargs (Any)

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

• _fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

• values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

!!! abstract “Usage Documentation”

[model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update (Mapping[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#modelmodel_dump)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

• mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to use the field’s alias in the dictionary key if defined.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

dict[str, Any]

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

• indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to serialize using field aliases.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

str

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'address': FieldInfo(annotation=AddressDetails, required=True), 'other_info': FieldInfo(annotation=OtherInformation, required=True), 'parents': FieldInfo(annotation=ParentInformation, required=True), 'personal': FieldInfo(annotation=PersonalDetails, required=True), 'qualifying_exam': FieldInfo(annotation=QualifyingExamination, required=True)}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')

Generates a JSON schema for a model class.

Parameters:

• by_alias (bool) – Whether to use attribute aliases or not.

• ref_template (str) – The reference template.

• schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

• mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Return type:

str

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(context, /)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:

None

Parameters:

context (Any)

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

• force (bool) – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors (bool) – Whether to raise errors, defaults to True.

• _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

• _types_namespace (Mapping[str, Any] | None) – The types namespace, defaults to None.

Return type:

bool | None

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)

Validate a pydantic model instance.

Parameters:

• obj (Any) – The object to validate.

• strict (bool | None) – Whether to enforce types strictly.

• from_attributes (bool | None) – Whether to extract data from object attributes.

• context (Any | None) – Additional context to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data (str | bytes | bytearray) – The JSON data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)

Validate the given object with string data against the Pydantic model.

Parameters:

• obj (Any) – The object containing string data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• path (str | pathlib.Path)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod parse_obj(obj)

Return type:

Self

Parameters:

obj (Any)

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• b (str | bytes)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')

Return type:

Dict[str, Any]

Parameters:

• by_alias (bool)

• ref_template (str)

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)

Return type:

str

Parameters:

• by_alias (bool)

• ref_template (str)

• dumps_kwargs (Any)

classmethod update_forward_refs(**localns)

Return type:

None

Parameters:

localns (Any)

classmethod validate(value)

Return type:

Self

Parameters:

value (Any)

personal: PersonalDetails

other_info: OtherInformation

qualifying_exam: QualifyingExamination

parents: ParentInformation

address: AddressDetails

class pesuacademy.models.SeatingInformation(**data)

Bases: pydantic.main.BaseModel

Represents seating information in the PESU Academy system.

Parameters:

• name (str)

• course_code (str)

• date (str)

• time (str)

• terminal (str)

• block (str)

name

Name of the course/assessment.

Type:

str

course_code

Code of the course for which seating information is provided.

Type:

str

date

Date of the examination or event.

Type:

str

time

Time of the examination or event.

Type:

str

terminal

Terminal or location where the student is seated.

Type:

str

block

Block or section of the seating arrangement.

Type:

str

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

Parameters:

memo (dict[int, Any] | None)

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

• core_schema (Union[InvalidSchema, AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, ArgumentsV3Schema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.

• handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

dict[str, Any]

Returns:

A JSON schema, as a Python object.

__init__(**data)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

data (Any)

Return type:

None

__iter__()

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__pretty__(fmt, **kwargs)

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:

Generator[Any, None, None]

Parameters:

• fmt (Callable[[Any], Any])

• kwargs (Any)

classmethod __pydantic_init_subclass__(**kwargs)

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:

**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.

Return type:

None

__repr_name__()

Name of the instance’s class, used in __repr__.

Return type:

str

__repr_recursion__(object)

Returns the string representation of a recursive object.

Return type:

str

Parameters:

object (Any)

__rich_repr__()

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:

Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

classmethod construct(_fields_set=None, **values)

Return type:

Self

Parameters:

• _fields_set (set[str] | None)

• values (Any)

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

• include – Optional set or mapping specifying which fields to include in the copied model.

• exclude – Optional set or mapping specifying which fields to exclude in the copied model.

• update – Optional dictionary of field-value pairs to override field values in the copied model.

• deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Return type:

Dict[str, Any]

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

classmethod from_orm(obj)

Return type:

Self

Parameters:

obj (Any)

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)

Return type:

str

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

• encoder (Callable[[Any], Any] | None)

• models_as_dict (bool)

• dumps_kwargs (Any)

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

• _fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

• values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

!!! abstract “Usage Documentation”

[model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update (Mapping[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#modelmodel_dump)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

• mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to use the field’s alias in the dictionary key if defined.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

dict[str, Any]

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

• indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to serialize using field aliases.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

str

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'block': FieldInfo(annotation=str, required=True), 'course_code': FieldInfo(annotation=str, required=True), 'date': FieldInfo(annotation=str, required=True), 'name': FieldInfo(annotation=str, required=True), 'terminal': FieldInfo(annotation=str, required=True), 'time': FieldInfo(annotation=str, required=True)}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')

Generates a JSON schema for a model class.

Parameters:

• by_alias (bool) – Whether to use attribute aliases or not.

• ref_template (str) – The reference template.

• schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

• mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Return type:

str

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(context, /)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:

None

Parameters:

context (Any)

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

• force (bool) – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors (bool) – Whether to raise errors, defaults to True.

• _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

• _types_namespace (Mapping[str, Any] | None) – The types namespace, defaults to None.

Return type:

bool | None

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)

Validate a pydantic model instance.

Parameters:

• obj (Any) – The object to validate.

• strict (bool | None) – Whether to enforce types strictly.

• from_attributes (bool | None) – Whether to extract data from object attributes.

• context (Any | None) – Additional context to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data (str | bytes | bytearray) – The JSON data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)

Validate the given object with string data against the Pydantic model.

Parameters:

• obj (Any) – The object containing string data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• path (str | pathlib.Path)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod parse_obj(obj)

Return type:

Self

Parameters:

obj (Any)

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• b (str | bytes)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')

Return type:

Dict[str, Any]

Parameters:

• by_alias (bool)

• ref_template (str)

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)

Return type:

str

Parameters:

• by_alias (bool)

• ref_template (str)

• dumps_kwargs (Any)

classmethod update_forward_refs(**localns)

Return type:

None

Parameters:

localns (Any)

classmethod validate(value)

Return type:

Self

Parameters:

value (Any)

name: str

course_code: str

date: str

time: str

terminal: str

block: str

class pesuacademy.models.SemesterResult(**data)

Bases: pydantic.main.BaseModel

Represents the result of a semester in the PESU Academy system.

Parameters:

• sgpa (str)

• credits (pesuacademy.models.results.Credits | None)

• courses (list[pesuacademy.models.results.CourseResult])

semester

Semester number.

Type:

str

sgpa

Semester Grade Point Average.

Type:

str

credits

Credits information for the semester, if available.

Type:

Optional[Credits]

courses

List of course results for the semester.

Type:

List[CourseResult]

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

Parameters:

memo (dict[int, Any] | None)

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

• core_schema (Union[InvalidSchema, AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, ArgumentsV3Schema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.

• handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

dict[str, Any]

Returns:

A JSON schema, as a Python object.

__init__(**data)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

data (Any)

Return type:

None

__iter__()

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__pretty__(fmt, **kwargs)

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:

Generator[Any, None, None]

Parameters:

• fmt (Callable[[Any], Any])

• kwargs (Any)

classmethod __pydantic_init_subclass__(**kwargs)

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:

**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.

Return type:

None

__repr_name__()

Name of the instance’s class, used in __repr__.

Return type:

str

__repr_recursion__(object)

Returns the string representation of a recursive object.

Return type:

str

Parameters:

object (Any)

__rich_repr__()

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:

Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

classmethod construct(_fields_set=None, **values)

Return type:

Self

Parameters:

• _fields_set (set[str] | None)

• values (Any)

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

• include – Optional set or mapping specifying which fields to include in the copied model.

• exclude – Optional set or mapping specifying which fields to exclude in the copied model.

• update – Optional dictionary of field-value pairs to override field values in the copied model.

• deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Return type:

Dict[str, Any]

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

classmethod from_orm(obj)

Return type:

Self

Parameters:

obj (Any)

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)

Return type:

str

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

• encoder (Callable[[Any], Any] | None)

• models_as_dict (bool)

• dumps_kwargs (Any)

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

• _fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

• values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

!!! abstract “Usage Documentation”

[model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update (Mapping[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#modelmodel_dump)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

• mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to use the field’s alias in the dictionary key if defined.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

dict[str, Any]

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

• indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to serialize using field aliases.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

str

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'courses': FieldInfo(annotation=list[CourseResult], required=True), 'credits': FieldInfo(annotation=Union[Credits, NoneType], required=False, default=None), 'sgpa': FieldInfo(annotation=str, required=True)}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')

Generates a JSON schema for a model class.

Parameters:

• by_alias (bool) – Whether to use attribute aliases or not.

• ref_template (str) – The reference template.

• schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

• mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Return type:

str

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(context, /)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:

None

Parameters:

context (Any)

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

• force (bool) – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors (bool) – Whether to raise errors, defaults to True.

• _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

• _types_namespace (Mapping[str, Any] | None) – The types namespace, defaults to None.

Return type:

bool | None

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)

Validate a pydantic model instance.

Parameters:

• obj (Any) – The object to validate.

• strict (bool | None) – Whether to enforce types strictly.

• from_attributes (bool | None) – Whether to extract data from object attributes.

• context (Any | None) – Additional context to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data (str | bytes | bytearray) – The JSON data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)

Validate the given object with string data against the Pydantic model.

Parameters:

• obj (Any) – The object containing string data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• path (str | pathlib.Path)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod parse_obj(obj)

Return type:

Self

Parameters:

obj (Any)

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• b (str | bytes)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')

Return type:

Dict[str, Any]

Parameters:

• by_alias (bool)

• ref_template (str)

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)

Return type:

str

Parameters:

• by_alias (bool)

• ref_template (str)

• dumps_kwargs (Any)

classmethod update_forward_refs(**localns)

Return type:

None

Parameters:

localns (Any)

classmethod validate(value)

Return type:

Self

Parameters:

value (Any)

sgpa: str

credits: Credits | None

courses: list[CourseResult]

class pesuacademy.models.Topic(**data)

Bases: pydantic.main.BaseModel

Represents a topic within a unit in the PESU Academy system.

Parameters:

• title (str)

• id (str)

• course_id (str)

• unit_id (str)

title

The title of the topic.

Type:

str

topic_id

Unique identifier for the topic.

Type:

str

course_id

Identifier for the course this topic belongs to.

Type:

str

unit_id

Identifier for the unit this topic belongs to.

Type:

str

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

Parameters:

memo (dict[int, Any] | None)

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

• core_schema (Union[InvalidSchema, AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, ArgumentsV3Schema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.

• handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

dict[str, Any]

Returns:

A JSON schema, as a Python object.

__init__(**data)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

data (Any)

Return type:

None

__iter__()

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__pretty__(fmt, **kwargs)

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:

Generator[Any, None, None]

Parameters:

• fmt (Callable[[Any], Any])

• kwargs (Any)

classmethod __pydantic_init_subclass__(**kwargs)

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:

**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.

Return type:

None

__repr_name__()

Name of the instance’s class, used in __repr__.

Return type:

str

__repr_recursion__(object)

Returns the string representation of a recursive object.

Return type:

str

Parameters:

object (Any)

__rich_repr__()

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:

Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

classmethod construct(_fields_set=None, **values)

Return type:

Self

Parameters:

• _fields_set (set[str] | None)

• values (Any)

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

• include – Optional set or mapping specifying which fields to include in the copied model.

• exclude – Optional set or mapping specifying which fields to exclude in the copied model.

• update – Optional dictionary of field-value pairs to override field values in the copied model.

• deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Return type:

Dict[str, Any]

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

classmethod from_orm(obj)

Return type:

Self

Parameters:

obj (Any)

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)

Return type:

str

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

• encoder (Callable[[Any], Any] | None)

• models_as_dict (bool)

• dumps_kwargs (Any)

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

• _fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

• values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

!!! abstract “Usage Documentation”

[model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update (Mapping[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#modelmodel_dump)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

• mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to use the field’s alias in the dictionary key if defined.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

dict[str, Any]

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

• indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to serialize using field aliases.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

str

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'course_id': FieldInfo(annotation=str, required=True), 'id': FieldInfo(annotation=str, required=True), 'title': FieldInfo(annotation=str, required=True), 'unit_id': FieldInfo(annotation=str, required=True)}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')

Generates a JSON schema for a model class.

Parameters:

• by_alias (bool) – Whether to use attribute aliases or not.

• ref_template (str) – The reference template.

• schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

• mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Return type:

str

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(context, /)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:

None

Parameters:

context (Any)

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

• force (bool) – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors (bool) – Whether to raise errors, defaults to True.

• _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

• _types_namespace (Mapping[str, Any] | None) – The types namespace, defaults to None.

Return type:

bool | None

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)

Validate a pydantic model instance.

Parameters:

• obj (Any) – The object to validate.

• strict (bool | None) – Whether to enforce types strictly.

• from_attributes (bool | None) – Whether to extract data from object attributes.

• context (Any | None) – Additional context to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data (str | bytes | bytearray) – The JSON data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)

Validate the given object with string data against the Pydantic model.

Parameters:

• obj (Any) – The object containing string data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• path (str | pathlib.Path)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod parse_obj(obj)

Return type:

Self

Parameters:

obj (Any)

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• b (str | bytes)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')

Return type:

Dict[str, Any]

Parameters:

• by_alias (bool)

• ref_template (str)

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)

Return type:

str

Parameters:

• by_alias (bool)

• ref_template (str)

• dumps_kwargs (Any)

classmethod update_forward_refs(**localns)

Return type:

None

Parameters:

localns (Any)

classmethod validate(value)

Return type:

Self

Parameters:

value (Any)

title: str

id: str

course_id: str

unit_id: str

class pesuacademy.models.Unit(**data)

Bases: pydantic.main.BaseModel

Represents a unit of a course in the PESU Academy system.

Parameters:

• title (str)

• id (str)

title

The title of the unit.

Type:

str

unit_id

Unique identifier for the unit.

Type:

str

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

Parameters:

memo (dict[int, Any] | None)

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

• core_schema (Union[InvalidSchema, AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, ArgumentsV3Schema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.

• handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

dict[str, Any]

Returns:

A JSON schema, as a Python object.

__init__(**data)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

data (Any)

Return type:

None

__iter__()

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__pretty__(fmt, **kwargs)

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:

Generator[Any, None, None]

Parameters:

• fmt (Callable[[Any], Any])

• kwargs (Any)

classmethod __pydantic_init_subclass__(**kwargs)

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:

**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.

Return type:

None

__repr_name__()

Name of the instance’s class, used in __repr__.

Return type:

str

__repr_recursion__(object)

Returns the string representation of a recursive object.

Return type:

str

Parameters:

object (Any)

__rich_repr__()

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:

Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

classmethod construct(_fields_set=None, **values)

Return type:

Self

Parameters:

• _fields_set (set[str] | None)

• values (Any)

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

• include – Optional set or mapping specifying which fields to include in the copied model.

• exclude – Optional set or mapping specifying which fields to exclude in the copied model.

• update – Optional dictionary of field-value pairs to override field values in the copied model.

• deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Return type:

Dict[str, Any]

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

classmethod from_orm(obj)

Return type:

Self

Parameters:

obj (Any)

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)

Return type:

str

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

• encoder (Callable[[Any], Any] | None)

• models_as_dict (bool)

• dumps_kwargs (Any)

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

• _fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

• values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

!!! abstract “Usage Documentation”

[model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update (Mapping[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#modelmodel_dump)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

• mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to use the field’s alias in the dictionary key if defined.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

dict[str, Any]

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

• indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to serialize using field aliases.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

str

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'id': FieldInfo(annotation=str, required=True), 'title': FieldInfo(annotation=str, required=True)}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')

Generates a JSON schema for a model class.

Parameters:

• by_alias (bool) – Whether to use attribute aliases or not.

• ref_template (str) – The reference template.

• schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

• mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Return type:

str

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(context, /)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:

None

Parameters:

context (Any)

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

• force (bool) – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors (bool) – Whether to raise errors, defaults to True.

• _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

• _types_namespace (Mapping[str, Any] | None) – The types namespace, defaults to None.

Return type:

bool | None

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)

Validate a pydantic model instance.

Parameters:

• obj (Any) – The object to validate.

• strict (bool | None) – Whether to enforce types strictly.

• from_attributes (bool | None) – Whether to extract data from object attributes.

• context (Any | None) – Additional context to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data (str | bytes | bytearray) – The JSON data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)

Validate the given object with string data against the Pydantic model.

Parameters:

• obj (Any) – The object containing string data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• path (str | pathlib.Path)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod parse_obj(obj)

Return type:

Self

Parameters:

obj (Any)

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• b (str | bytes)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')

Return type:

Dict[str, Any]

Parameters:

• by_alias (bool)

• ref_template (str)

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)

Return type:

str

Parameters:

• by_alias (bool)

• ref_template (str)

• dumps_kwargs (Any)

classmethod update_forward_refs(**localns)

Return type:

None

Parameters:

localns (Any)

classmethod validate(value)

Return type:

Self

Parameters:

value (Any)

title: str

id: str

class pesuacademy.models.AddressDetails(**data)

Bases: pydantic.main.BaseModel

Represents address details in the PESU Academy system.

Parameters:

• present (str)

• permanent (str)

present

Present address of the user.

Type:

str

permanent

Permanent address of the user.

Type:

str

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

Parameters:

memo (dict[int, Any] | None)

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

• core_schema (Union[InvalidSchema, AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, ArgumentsV3Schema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.

• handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

dict[str, Any]

Returns:

A JSON schema, as a Python object.

__init__(**data)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

data (Any)

Return type:

None

__iter__()

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__pretty__(fmt, **kwargs)

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:

Generator[Any, None, None]

Parameters:

• fmt (Callable[[Any], Any])

• kwargs (Any)

classmethod __pydantic_init_subclass__(**kwargs)

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:

**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.

Return type:

None

__repr_name__()

Name of the instance’s class, used in __repr__.

Return type:

str

__repr_recursion__(object)

Returns the string representation of a recursive object.

Return type:

str

Parameters:

object (Any)

__rich_repr__()

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:

Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

classmethod construct(_fields_set=None, **values)

Return type:

Self

Parameters:

• _fields_set (set[str] | None)

• values (Any)

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

• include – Optional set or mapping specifying which fields to include in the copied model.

• exclude – Optional set or mapping specifying which fields to exclude in the copied model.

• update – Optional dictionary of field-value pairs to override field values in the copied model.

• deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Return type:

Dict[str, Any]

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

classmethod from_orm(obj)

Return type:

Self

Parameters:

obj (Any)

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)

Return type:

str

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

• encoder (Callable[[Any], Any] | None)

• models_as_dict (bool)

• dumps_kwargs (Any)

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

• _fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

• values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

!!! abstract “Usage Documentation”

[model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update (Mapping[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#modelmodel_dump)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

• mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to use the field’s alias in the dictionary key if defined.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

dict[str, Any]

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

• indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to serialize using field aliases.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

str

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'permanent': FieldInfo(annotation=str, required=True), 'present': FieldInfo(annotation=str, required=True)}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')

Generates a JSON schema for a model class.

Parameters:

• by_alias (bool) – Whether to use attribute aliases or not.

• ref_template (str) – The reference template.

• schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

• mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Return type:

str

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(context, /)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:

None

Parameters:

context (Any)

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

• force (bool) – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors (bool) – Whether to raise errors, defaults to True.

• _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

• _types_namespace (Mapping[str, Any] | None) – The types namespace, defaults to None.

Return type:

bool | None

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)

Validate a pydantic model instance.

Parameters:

• obj (Any) – The object to validate.

• strict (bool | None) – Whether to enforce types strictly.

• from_attributes (bool | None) – Whether to extract data from object attributes.

• context (Any | None) – Additional context to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data (str | bytes | bytearray) – The JSON data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)

Validate the given object with string data against the Pydantic model.

Parameters:

• obj (Any) – The object containing string data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• path (str | pathlib.Path)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod parse_obj(obj)

Return type:

Self

Parameters:

obj (Any)

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• b (str | bytes)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')

Return type:

Dict[str, Any]

Parameters:

• by_alias (bool)

• ref_template (str)

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)

Return type:

str

Parameters:

• by_alias (bool)

• ref_template (str)

• dumps_kwargs (Any)

classmethod update_forward_refs(**localns)

Return type:

None

Parameters:

localns (Any)

classmethod validate(value)

Return type:

Self

Parameters:

value (Any)

present: str

permanent: str

class pesuacademy.models.OtherInformation(**data)

Bases: pydantic.main.BaseModel

Represents other personal information of a user in the PESU Academy system.

Parameters:

• sslc_marks (str)

• puc_marks (str)

• date_of_birth (str)

• blood_group (str)

sslc_marks

Marks obtained in SSLC.

Type:

str

puc_marks

Marks obtained in PUC.

Type:

str

date_of_birth

Date of birth of the user.

Type:

str

blood_group

Blood group of the user.

Type:

str

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

Parameters:

memo (dict[int, Any] | None)

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

• core_schema (Union[InvalidSchema, AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, ArgumentsV3Schema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.

• handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

dict[str, Any]

Returns:

A JSON schema, as a Python object.

__init__(**data)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

data (Any)

Return type:

None

__iter__()

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__pretty__(fmt, **kwargs)

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:

Generator[Any, None, None]

Parameters:

• fmt (Callable[[Any], Any])

• kwargs (Any)

classmethod __pydantic_init_subclass__(**kwargs)

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:

**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.

Return type:

None

__repr_name__()

Name of the instance’s class, used in __repr__.

Return type:

str

__repr_recursion__(object)

Returns the string representation of a recursive object.

Return type:

str

Parameters:

object (Any)

__rich_repr__()

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:

Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

classmethod construct(_fields_set=None, **values)

Return type:

Self

Parameters:

• _fields_set (set[str] | None)

• values (Any)

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

• include – Optional set or mapping specifying which fields to include in the copied model.

• exclude – Optional set or mapping specifying which fields to exclude in the copied model.

• update – Optional dictionary of field-value pairs to override field values in the copied model.

• deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Return type:

Dict[str, Any]

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

classmethod from_orm(obj)

Return type:

Self

Parameters:

obj (Any)

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)

Return type:

str

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

• encoder (Callable[[Any], Any] | None)

• models_as_dict (bool)

• dumps_kwargs (Any)

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

• _fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

• values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

!!! abstract “Usage Documentation”

[model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update (Mapping[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#modelmodel_dump)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

• mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to use the field’s alias in the dictionary key if defined.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

dict[str, Any]

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

• indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to serialize using field aliases.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

str

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'blood_group': FieldInfo(annotation=str, required=True), 'date_of_birth': FieldInfo(annotation=str, required=True), 'puc_marks': FieldInfo(annotation=str, required=True), 'sslc_marks': FieldInfo(annotation=str, required=True)}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')

Generates a JSON schema for a model class.

Parameters:

• by_alias (bool) – Whether to use attribute aliases or not.

• ref_template (str) – The reference template.

• schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

• mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Return type:

str

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(context, /)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:

None

Parameters:

context (Any)

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

• force (bool) – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors (bool) – Whether to raise errors, defaults to True.

• _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

• _types_namespace (Mapping[str, Any] | None) – The types namespace, defaults to None.

Return type:

bool | None

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)

Validate a pydantic model instance.

Parameters:

• obj (Any) – The object to validate.

• strict (bool | None) – Whether to enforce types strictly.

• from_attributes (bool | None) – Whether to extract data from object attributes.

• context (Any | None) – Additional context to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data (str | bytes | bytearray) – The JSON data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)

Validate the given object with string data against the Pydantic model.

Parameters:

• obj (Any) – The object containing string data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• path (str | pathlib.Path)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod parse_obj(obj)

Return type:

Self

Parameters:

obj (Any)

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• b (str | bytes)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')

Return type:

Dict[str, Any]

Parameters:

• by_alias (bool)

• ref_template (str)

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)

Return type:

str

Parameters:

• by_alias (bool)

• ref_template (str)

• dumps_kwargs (Any)

classmethod update_forward_refs(**localns)

Return type:

None

Parameters:

localns (Any)

classmethod validate(value)

Return type:

Self

Parameters:

value (Any)

sslc_marks: str

puc_marks: str

date_of_birth: str

blood_group: str

class pesuacademy.models.ParentDetails(**data)

Bases: pydantic.main.BaseModel

Represents details of a parent in the PESU Academy system.

Parameters:

• name (str)

• mobile (str)

• email (str)

• occupation (str)

• qualification (str)

• designation (str)

• employer (str)

name

Full name of the parent.

Type:

str

mobile

Mobile number of the parent.

Type:

str

email

Email address of the parent.

Type:

str

occupation

Occupation of the parent.

Type:

str

qualification

Qualification of the parent.

Type:

str

designation

Designation of the parent.

Type:

str

employer

Employer of the parent.

Type:

str

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

Parameters:

memo (dict[int, Any] | None)

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

• core_schema (Union[InvalidSchema, AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, ArgumentsV3Schema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.

• handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

dict[str, Any]

Returns:

A JSON schema, as a Python object.

__init__(**data)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

data (Any)

Return type:

None

__iter__()

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__pretty__(fmt, **kwargs)

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:

Generator[Any, None, None]

Parameters:

• fmt (Callable[[Any], Any])

• kwargs (Any)

classmethod __pydantic_init_subclass__(**kwargs)

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:

**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.

Return type:

None

__repr_name__()

Name of the instance’s class, used in __repr__.

Return type:

str

__repr_recursion__(object)

Returns the string representation of a recursive object.

Return type:

str

Parameters:

object (Any)

__rich_repr__()

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:

Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

classmethod construct(_fields_set=None, **values)

Return type:

Self

Parameters:

• _fields_set (set[str] | None)

• values (Any)

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

• include – Optional set or mapping specifying which fields to include in the copied model.

• exclude – Optional set or mapping specifying which fields to exclude in the copied model.

• update – Optional dictionary of field-value pairs to override field values in the copied model.

• deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Return type:

Dict[str, Any]

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

classmethod from_orm(obj)

Return type:

Self

Parameters:

obj (Any)

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)

Return type:

str

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

• encoder (Callable[[Any], Any] | None)

• models_as_dict (bool)

• dumps_kwargs (Any)

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

• _fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

• values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

!!! abstract “Usage Documentation”

[model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update (Mapping[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#modelmodel_dump)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

• mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to use the field’s alias in the dictionary key if defined.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

dict[str, Any]

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

• indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to serialize using field aliases.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

str

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'designation': FieldInfo(annotation=str, required=True), 'email': FieldInfo(annotation=str, required=True), 'employer': FieldInfo(annotation=str, required=True), 'mobile': FieldInfo(annotation=str, required=True), 'name': FieldInfo(annotation=str, required=True), 'occupation': FieldInfo(annotation=str, required=True), 'qualification': FieldInfo(annotation=str, required=True)}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')

Generates a JSON schema for a model class.

Parameters:

• by_alias (bool) – Whether to use attribute aliases or not.

• ref_template (str) – The reference template.

• schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

• mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Return type:

str

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(context, /)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:

None

Parameters:

context (Any)

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

• force (bool) – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors (bool) – Whether to raise errors, defaults to True.

• _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

• _types_namespace (Mapping[str, Any] | None) – The types namespace, defaults to None.

Return type:

bool | None

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)

Validate a pydantic model instance.

Parameters:

• obj (Any) – The object to validate.

• strict (bool | None) – Whether to enforce types strictly.

• from_attributes (bool | None) – Whether to extract data from object attributes.

• context (Any | None) – Additional context to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data (str | bytes | bytearray) – The JSON data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)

Validate the given object with string data against the Pydantic model.

Parameters:

• obj (Any) – The object containing string data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• path (str | pathlib.Path)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod parse_obj(obj)

Return type:

Self

Parameters:

obj (Any)

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• b (str | bytes)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')

Return type:

Dict[str, Any]

Parameters:

• by_alias (bool)

• ref_template (str)

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)

Return type:

str

Parameters:

• by_alias (bool)

• ref_template (str)

• dumps_kwargs (Any)

classmethod update_forward_refs(**localns)

Return type:

None

Parameters:

localns (Any)

classmethod validate(value)

Return type:

Self

Parameters:

value (Any)

name: str

mobile: str

email: str

occupation: str

qualification: str

designation: str

employer: str

class pesuacademy.models.ParentInformation(**data)

Bases: pydantic.main.BaseModel

Represents information about parents in the PESU Academy system.

Parameters:

• father (pesuacademy.models.profile.ParentDetails)

• mother (pesuacademy.models.profile.ParentDetails)

father

Details of the father.

Type:

ParentDetails

mother

Details of the mother.

Type:

ParentDetails

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

Parameters:

memo (dict[int, Any] | None)

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

• core_schema (Union[InvalidSchema, AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, ArgumentsV3Schema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.

• handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

dict[str, Any]

Returns:

A JSON schema, as a Python object.

__init__(**data)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

data (Any)

Return type:

None

__iter__()

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__pretty__(fmt, **kwargs)

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:

Generator[Any, None, None]

Parameters:

• fmt (Callable[[Any], Any])

• kwargs (Any)

classmethod __pydantic_init_subclass__(**kwargs)

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:

**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.

Return type:

None

__repr_name__()

Name of the instance’s class, used in __repr__.

Return type:

str

__repr_recursion__(object)

Returns the string representation of a recursive object.

Return type:

str

Parameters:

object (Any)

__rich_repr__()

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:

Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

classmethod construct(_fields_set=None, **values)

Return type:

Self

Parameters:

• _fields_set (set[str] | None)

• values (Any)

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

• include – Optional set or mapping specifying which fields to include in the copied model.

• exclude – Optional set or mapping specifying which fields to exclude in the copied model.

• update – Optional dictionary of field-value pairs to override field values in the copied model.

• deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Return type:

Dict[str, Any]

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

classmethod from_orm(obj)

Return type:

Self

Parameters:

obj (Any)

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)

Return type:

str

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

• encoder (Callable[[Any], Any] | None)

• models_as_dict (bool)

• dumps_kwargs (Any)

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

• _fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

• values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

!!! abstract “Usage Documentation”

[model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update (Mapping[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#modelmodel_dump)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

• mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to use the field’s alias in the dictionary key if defined.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

dict[str, Any]

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

• indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to serialize using field aliases.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

str

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'father': FieldInfo(annotation=ParentDetails, required=True), 'mother': FieldInfo(annotation=ParentDetails, required=True)}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')

Generates a JSON schema for a model class.

Parameters:

• by_alias (bool) – Whether to use attribute aliases or not.

• ref_template (str) – The reference template.

• schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

• mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Return type:

str

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(context, /)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:

None

Parameters:

context (Any)

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

• force (bool) – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors (bool) – Whether to raise errors, defaults to True.

• _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

• _types_namespace (Mapping[str, Any] | None) – The types namespace, defaults to None.

Return type:

bool | None

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)

Validate a pydantic model instance.

Parameters:

• obj (Any) – The object to validate.

• strict (bool | None) – Whether to enforce types strictly.

• from_attributes (bool | None) – Whether to extract data from object attributes.

• context (Any | None) – Additional context to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data (str | bytes | bytearray) – The JSON data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)

Validate the given object with string data against the Pydantic model.

Parameters:

• obj (Any) – The object containing string data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• path (str | pathlib.Path)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod parse_obj(obj)

Return type:

Self

Parameters:

obj (Any)

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• b (str | bytes)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')

Return type:

Dict[str, Any]

Parameters:

• by_alias (bool)

• ref_template (str)

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)

Return type:

str

Parameters:

• by_alias (bool)

• ref_template (str)

• dumps_kwargs (Any)

classmethod update_forward_refs(**localns)

Return type:

None

Parameters:

localns (Any)

classmethod validate(value)

Return type:

Self

Parameters:

value (Any)

father: ParentDetails

mother: ParentDetails

class pesuacademy.models.PersonalDetails(**data)

Bases: pydantic.main.BaseModel

Represents personal details of a user in the PESU Academy system.

Parameters:

• name (str)

• pesu_id (str)

• srn (str)

• program (str)

• branch (str)

• semester (str)

• section (str)

• email_id (str)

• contact_no (str)

• aadhar_no (str | None)

• name_as_in_aadhar (str | None)

• image (str | None)

name

Full name of the user.

Type:

str

pesu_id

PESU ID of the user. (PRN or similar)

Type:

str

srn

SRN of the user.

Type:

str

program

Program enrolled in by the user.

Type:

str

branch

Branch of study.

Type:

str

semester

Current semester.

Type:

str

section

Section of the user.

Type:

str

email_id

Email address of the user.

Type:

str

contact_no

Contact number of the user.

Type:

str

name_as_in_aadhar

Name as per Aadhar card.

Type:

str

image

Base64 encoded profile image, if available.

Type:

Optional[str]

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

Parameters:

memo (dict[int, Any] | None)

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

• core_schema (Union[InvalidSchema, AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, ArgumentsV3Schema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.

• handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

dict[str, Any]

Returns:

A JSON schema, as a Python object.

__init__(**data)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

data (Any)

Return type:

None

__iter__()

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__pretty__(fmt, **kwargs)

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:

Generator[Any, None, None]

Parameters:

• fmt (Callable[[Any], Any])

• kwargs (Any)

classmethod __pydantic_init_subclass__(**kwargs)

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:

**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.

Return type:

None

__repr_name__()

Name of the instance’s class, used in __repr__.

Return type:

str

__repr_recursion__(object)

Returns the string representation of a recursive object.

Return type:

str

Parameters:

object (Any)

__rich_repr__()

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:

Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

classmethod construct(_fields_set=None, **values)

Return type:

Self

Parameters:

• _fields_set (set[str] | None)

• values (Any)

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

• include – Optional set or mapping specifying which fields to include in the copied model.

• exclude – Optional set or mapping specifying which fields to exclude in the copied model.

• update – Optional dictionary of field-value pairs to override field values in the copied model.

• deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Return type:

Dict[str, Any]

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

classmethod from_orm(obj)

Return type:

Self

Parameters:

obj (Any)

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)

Return type:

str

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

• encoder (Callable[[Any], Any] | None)

• models_as_dict (bool)

• dumps_kwargs (Any)

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

• _fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

• values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

!!! abstract “Usage Documentation”

[model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update (Mapping[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#modelmodel_dump)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

• mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to use the field’s alias in the dictionary key if defined.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

dict[str, Any]

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

• indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to serialize using field aliases.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

str

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'aadhar_no': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'branch': FieldInfo(annotation=str, required=True), 'contact_no': FieldInfo(annotation=str, required=True), 'email_id': FieldInfo(annotation=str, required=True), 'image': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'name': FieldInfo(annotation=str, required=True), 'name_as_in_aadhar': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'pesu_id': FieldInfo(annotation=str, required=True), 'program': FieldInfo(annotation=str, required=True), 'section': FieldInfo(annotation=str, required=True), 'semester': FieldInfo(annotation=str, required=True), 'srn': FieldInfo(annotation=str, required=True)}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')

Generates a JSON schema for a model class.

Parameters:

• by_alias (bool) – Whether to use attribute aliases or not.

• ref_template (str) – The reference template.

• schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

• mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Return type:

str

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(context, /)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:

None

Parameters:

context (Any)

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

• force (bool) – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors (bool) – Whether to raise errors, defaults to True.

• _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

• _types_namespace (Mapping[str, Any] | None) – The types namespace, defaults to None.

Return type:

bool | None

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)

Validate a pydantic model instance.

Parameters:

• obj (Any) – The object to validate.

• strict (bool | None) – Whether to enforce types strictly.

• from_attributes (bool | None) – Whether to extract data from object attributes.

• context (Any | None) – Additional context to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data (str | bytes | bytearray) – The JSON data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)

Validate the given object with string data against the Pydantic model.

Parameters:

• obj (Any) – The object containing string data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• path (str | pathlib.Path)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod parse_obj(obj)

Return type:

Self

Parameters:

obj (Any)

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• b (str | bytes)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')

Return type:

Dict[str, Any]

Parameters:

• by_alias (bool)

• ref_template (str)

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)

Return type:

str

Parameters:

• by_alias (bool)

• ref_template (str)

• dumps_kwargs (Any)

classmethod update_forward_refs(**localns)

Return type:

None

Parameters:

localns (Any)

classmethod validate(value)

Return type:

Self

Parameters:

value (Any)

name: str

pesu_id: str

srn: str

program: str

branch: str

semester: str

section: str

email_id: str

contact_no: str

aadhar_no: str | None

name_as_in_aadhar: str | None

image: str | None

class pesuacademy.models.QualifyingExamination(**data)

Bases: pydantic.main.BaseModel

Represents details of a qualifying examination in the PESU Academy system.

Parameters:

• exam (str)

• rank (str)

• score (str | None)

exam

The qualifying examination.

Type:

str

rank

Rank obtained in the examination.

Type:

str

score

Score obtained in the examination.

Type:

str

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

Parameters:

memo (dict[int, Any] | None)

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

• core_schema (Union[InvalidSchema, AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, ArgumentsV3Schema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.

• handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

dict[str, Any]

Returns:

A JSON schema, as a Python object.

__init__(**data)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

data (Any)

Return type:

None

__iter__()

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__pretty__(fmt, **kwargs)

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:

Generator[Any, None, None]

Parameters:

• fmt (Callable[[Any], Any])

• kwargs (Any)

classmethod __pydantic_init_subclass__(**kwargs)

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:

**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.

Return type:

None

__repr_name__()

Name of the instance’s class, used in __repr__.

Return type:

str

__repr_recursion__(object)

Returns the string representation of a recursive object.

Return type:

str

Parameters:

object (Any)

__rich_repr__()

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:

Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

classmethod construct(_fields_set=None, **values)

Return type:

Self

Parameters:

• _fields_set (set[str] | None)

• values (Any)

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

• include – Optional set or mapping specifying which fields to include in the copied model.

• exclude – Optional set or mapping specifying which fields to exclude in the copied model.

• update – Optional dictionary of field-value pairs to override field values in the copied model.

• deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Return type:

Dict[str, Any]

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

classmethod from_orm(obj)

Return type:

Self

Parameters:

obj (Any)

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)

Return type:

str

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

• encoder (Callable[[Any], Any] | None)

• models_as_dict (bool)

• dumps_kwargs (Any)

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

• _fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

• values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

!!! abstract “Usage Documentation”

[model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update (Mapping[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#modelmodel_dump)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

• mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to use the field’s alias in the dictionary key if defined.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

dict[str, Any]

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

• indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to serialize using field aliases.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

str

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'exam': FieldInfo(annotation=str, required=True), 'rank': FieldInfo(annotation=str, required=True), 'score': FieldInfo(annotation=Union[str, NoneType], required=False, default=None)}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')

Generates a JSON schema for a model class.

Parameters:

• by_alias (bool) – Whether to use attribute aliases or not.

• ref_template (str) – The reference template.

• schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

• mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Return type:

str

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(context, /)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:

None

Parameters:

context (Any)

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

• force (bool) – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors (bool) – Whether to raise errors, defaults to True.

• _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

• _types_namespace (Mapping[str, Any] | None) – The types namespace, defaults to None.

Return type:

bool | None

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)

Validate a pydantic model instance.

Parameters:

• obj (Any) – The object to validate.

• strict (bool | None) – Whether to enforce types strictly.

• from_attributes (bool | None) – Whether to extract data from object attributes.

• context (Any | None) – Additional context to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data (str | bytes | bytearray) – The JSON data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)

Validate the given object with string data against the Pydantic model.

Parameters:

• obj (Any) – The object containing string data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• path (str | pathlib.Path)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod parse_obj(obj)

Return type:

Self

Parameters:

obj (Any)

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• b (str | bytes)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')

Return type:

Dict[str, Any]

Parameters:

• by_alias (bool)

• ref_template (str)

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)

Return type:

str

Parameters:

• by_alias (bool)

• ref_template (str)

• dumps_kwargs (Any)

classmethod update_forward_refs(**localns)

Return type:

None

Parameters:

localns (Any)

classmethod validate(value)

Return type:

Self

Parameters:

value (Any)

exam: str

rank: str

score: str | None

class pesuacademy.models.Assessment(**data)

Bases: pydantic.main.BaseModel

Represents an assessment(e.g., ISA1, MATLAB) in the PESU Academy system.

Parameters:

• name (str)

• marks (str | None)

• total (str | None)

name

Name of the assessment.

Type:

str

marks

Marks obtained in the assessment, if applicable. (e.g., 72)

Type:

Optional[str]

total

Total marks for the assessment, if applicable. (e.g., 100)

Type:

Optional[str]

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

Parameters:

memo (dict[int, Any] | None)

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

• core_schema (Union[InvalidSchema, AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, ArgumentsV3Schema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.

• handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

dict[str, Any]

Returns:

A JSON schema, as a Python object.

__init__(**data)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

data (Any)

Return type:

None

__iter__()

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__pretty__(fmt, **kwargs)

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:

Generator[Any, None, None]

Parameters:

• fmt (Callable[[Any], Any])

• kwargs (Any)

classmethod __pydantic_init_subclass__(**kwargs)

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:

**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.

Return type:

None

__repr_name__()

Name of the instance’s class, used in __repr__.

Return type:

str

__repr_recursion__(object)

Returns the string representation of a recursive object.

Return type:

str

Parameters:

object (Any)

__rich_repr__()

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:

Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

classmethod construct(_fields_set=None, **values)

Return type:

Self

Parameters:

• _fields_set (set[str] | None)

• values (Any)

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

• include – Optional set or mapping specifying which fields to include in the copied model.

• exclude – Optional set or mapping specifying which fields to exclude in the copied model.

• update – Optional dictionary of field-value pairs to override field values in the copied model.

• deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Return type:

Dict[str, Any]

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

classmethod from_orm(obj)

Return type:

Self

Parameters:

obj (Any)

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)

Return type:

str

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

• encoder (Callable[[Any], Any] | None)

• models_as_dict (bool)

• dumps_kwargs (Any)

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

• _fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

• values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

!!! abstract “Usage Documentation”

[model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update (Mapping[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#modelmodel_dump)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

• mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to use the field’s alias in the dictionary key if defined.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

dict[str, Any]

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

• indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to serialize using field aliases.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

str

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'marks': FieldInfo(annotation=Union[str, NoneType], required=False, default=None), 'name': FieldInfo(annotation=str, required=True), 'total': FieldInfo(annotation=Union[str, NoneType], required=False, default=None)}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')

Generates a JSON schema for a model class.

Parameters:

• by_alias (bool) – Whether to use attribute aliases or not.

• ref_template (str) – The reference template.

• schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

• mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Return type:

str

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(context, /)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:

None

Parameters:

context (Any)

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

• force (bool) – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors (bool) – Whether to raise errors, defaults to True.

• _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

• _types_namespace (Mapping[str, Any] | None) – The types namespace, defaults to None.

Return type:

bool | None

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)

Validate a pydantic model instance.

Parameters:

• obj (Any) – The object to validate.

• strict (bool | None) – Whether to enforce types strictly.

• from_attributes (bool | None) – Whether to extract data from object attributes.

• context (Any | None) – Additional context to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data (str | bytes | bytearray) – The JSON data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)

Validate the given object with string data against the Pydantic model.

Parameters:

• obj (Any) – The object containing string data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• path (str | pathlib.Path)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod parse_obj(obj)

Return type:

Self

Parameters:

obj (Any)

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• b (str | bytes)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')

Return type:

Dict[str, Any]

Parameters:

• by_alias (bool)

• ref_template (str)

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)

Return type:

str

Parameters:

• by_alias (bool)

• ref_template (str)

• dumps_kwargs (Any)

classmethod update_forward_refs(**localns)

Return type:

None

Parameters:

localns (Any)

classmethod validate(value)

Return type:

Self

Parameters:

value (Any)

name: str

marks: str | None

total: str | None

class pesuacademy.models.CourseResult(**data)

Bases: pydantic.main.BaseModel

Represents the result of a course in the PESU Academy system.

Parameters:

• code (str)

• title (str)

• credits (pesuacademy.models.results.Credits | None)

• assessments (list[pesuacademy.models.results.Assessment])

code

Unique identifier for the course.

Type:

str

title

Title of the course.

Type:

str

credits

Credits information for the course, if available.

Type:

Optional[Credits]

assessments

List of assessments associated with the course.

Type:

List[Assessment]

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

Parameters:

memo (dict[int, Any] | None)

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

• core_schema (Union[InvalidSchema, AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, ArgumentsV3Schema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.

• handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

dict[str, Any]

Returns:

A JSON schema, as a Python object.

__init__(**data)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

data (Any)

Return type:

None

__iter__()

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__pretty__(fmt, **kwargs)

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:

Generator[Any, None, None]

Parameters:

• fmt (Callable[[Any], Any])

• kwargs (Any)

classmethod __pydantic_init_subclass__(**kwargs)

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:

**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.

Return type:

None

__repr_name__()

Name of the instance’s class, used in __repr__.

Return type:

str

__repr_recursion__(object)

Returns the string representation of a recursive object.

Return type:

str

Parameters:

object (Any)

__rich_repr__()

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:

Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

classmethod construct(_fields_set=None, **values)

Return type:

Self

Parameters:

• _fields_set (set[str] | None)

• values (Any)

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

• include – Optional set or mapping specifying which fields to include in the copied model.

• exclude – Optional set or mapping specifying which fields to exclude in the copied model.

• update – Optional dictionary of field-value pairs to override field values in the copied model.

• deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Return type:

Dict[str, Any]

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

classmethod from_orm(obj)

Return type:

Self

Parameters:

obj (Any)

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)

Return type:

str

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

• encoder (Callable[[Any], Any] | None)

• models_as_dict (bool)

• dumps_kwargs (Any)

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

• _fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

• values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

!!! abstract “Usage Documentation”

[model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update (Mapping[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#modelmodel_dump)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

• mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to use the field’s alias in the dictionary key if defined.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

dict[str, Any]

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

• indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to serialize using field aliases.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

str

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'assessments': FieldInfo(annotation=list[Assessment], required=True), 'code': FieldInfo(annotation=str, required=True), 'credits': FieldInfo(annotation=Union[Credits, NoneType], required=False, default=None), 'title': FieldInfo(annotation=str, required=True)}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')

Generates a JSON schema for a model class.

Parameters:

• by_alias (bool) – Whether to use attribute aliases or not.

• ref_template (str) – The reference template.

• schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

• mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Return type:

str

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(context, /)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:

None

Parameters:

context (Any)

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

• force (bool) – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors (bool) – Whether to raise errors, defaults to True.

• _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

• _types_namespace (Mapping[str, Any] | None) – The types namespace, defaults to None.

Return type:

bool | None

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)

Validate a pydantic model instance.

Parameters:

• obj (Any) – The object to validate.

• strict (bool | None) – Whether to enforce types strictly.

• from_attributes (bool | None) – Whether to extract data from object attributes.

• context (Any | None) – Additional context to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data (str | bytes | bytearray) – The JSON data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)

Validate the given object with string data against the Pydantic model.

Parameters:

• obj (Any) – The object containing string data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• path (str | pathlib.Path)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod parse_obj(obj)

Return type:

Self

Parameters:

obj (Any)

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• b (str | bytes)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')

Return type:

Dict[str, Any]

Parameters:

• by_alias (bool)

• ref_template (str)

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)

Return type:

str

Parameters:

• by_alias (bool)

• ref_template (str)

• dumps_kwargs (Any)

classmethod update_forward_refs(**localns)

Return type:

None

Parameters:

localns (Any)

classmethod validate(value)

Return type:

Self

Parameters:

value (Any)

code: str

title: str

credits: Credits | None

assessments: list[Assessment]

class pesuacademy.models.Credits(**data)

Bases: pydantic.main.BaseModel

Represents credits information for a course in the PESU Academy system.

Parameters:

• earned (str)

• total (str)

earned

Credits earned for the course.

Type:

str

total

Total credits available for the course.

Type:

str

__copy__()

Returns a shallow copy of the model.

Return type:

Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Return type:

Self

Parameters:

memo (dict[int, Any] | None)

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

• core_schema (Union[InvalidSchema, AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, ArgumentsV3Schema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.

• handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

dict[str, Any]

Returns:

A JSON schema, as a Python object.

__init__(**data)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

data (Any)

Return type:

None

__iter__()

So dict(model) works.

Return type:

Generator[tuple[str, Any], None, None]

__pretty__(fmt, **kwargs)

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:

Generator[Any, None, None]

Parameters:

• fmt (Callable[[Any], Any])

• kwargs (Any)

classmethod __pydantic_init_subclass__(**kwargs)

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:

**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.

Return type:

None

__repr_name__()

Name of the instance’s class, used in __repr__.

Return type:

str

__repr_recursion__(object)

Returns the string representation of a recursive object.

Return type:

str

Parameters:

object (Any)

__rich_repr__()

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:

Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

classmethod construct(_fields_set=None, **values)

Return type:

Self

Parameters:

• _fields_set (set[str] | None)

• values (Any)

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

• include – Optional set or mapping specifying which fields to include in the copied model.

• exclude – Optional set or mapping specifying which fields to exclude in the copied model.

• update – Optional dictionary of field-value pairs to override field values in the copied model.

• deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Return type:

Dict[str, Any]

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

classmethod from_orm(obj)

Return type:

Self

Parameters:

obj (Any)

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)

Return type:

str

Parameters:

• include (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• exclude (set[int] | set[str] | collections.abc.Mapping[int, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | collections.abc.Mapping[str, set[int] | set[str] | collections.abc.Mapping[int, IncEx | bool] | collections.abc.Mapping[str, IncEx | bool] | bool] | None)

• by_alias (bool)

• exclude_unset (bool)

• exclude_defaults (bool)

• exclude_none (bool)

• encoder (Callable[[Any], Any] | None)

• models_as_dict (bool)

• dumps_kwargs (Any)

model_computed_fields = {}

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

• _fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

• values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)

!!! abstract “Usage Documentation”

[model_copy](../concepts/serialization.md#model_copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

• update (Mapping[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

• deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#modelmodel_dump)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

• mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to use the field’s alias in the dictionary key if defined.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

dict[str, Any]

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)

!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#modelmodel_dump_json)

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

• indent (int | None) – Indentation to use in the JSON output. If None is passed, the output will be compact.

• include (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.

• exclude (Union[set[int], set[str], Mapping[int, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], Mapping[str, Union[set[int], set[str], Mapping[int, Union[IncEx, bool]], Mapping[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.

• context (Any | None) – Additional context to pass to the serializer.

• by_alias (bool | None) – Whether to serialize using field aliases.

• exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.

• exclude_defaults (bool) – Whether to exclude fields that are set to their default value.

• exclude_none (bool) – Whether to exclude fields that have a value of None.

• round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

• warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

• fallback (Optional[Callable[[Any], Any]]) – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

• serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

str

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'earned': FieldInfo(annotation=str, required=True), 'total': FieldInfo(annotation=str, required=True)}

property model_fields_set: set[str]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')

Generates a JSON schema for a model class.

Parameters:

• by_alias (bool) – Whether to use attribute aliases or not.

• ref_template (str) – The reference template.

• schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

• mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

dict[str, Any]

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Return type:

str

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(context, /)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:

None

Parameters:

context (Any)

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

• force (bool) – Whether to force the rebuilding of the model schema, defaults to False.

• raise_errors (bool) – Whether to raise errors, defaults to True.

• _parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.

• _types_namespace (Mapping[str, Any] | None) – The types namespace, defaults to None.

Return type:

bool | None

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None, by_alias=None, by_name=None)

Validate a pydantic model instance.

Parameters:

• obj (Any) – The object to validate.

• strict (bool | None) – Whether to enforce types strictly.

• from_attributes (bool | None) – Whether to extract data from object attributes.

• context (Any | None) – Additional context to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None, by_alias=None, by_name=None)

!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

• json_data (str | bytes | bytearray) – The JSON data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None, by_alias=None, by_name=None)

Validate the given object with string data against the Pydantic model.

Parameters:

• obj (Any) – The object containing string data to validate.

• strict (bool | None) – Whether to enforce types strictly.

• context (Any | None) – Extra variables to pass to the validator.

• by_alias (bool | None) – Whether to use the field’s alias when validating against the provided input data.

• by_name (bool | None) – Whether to use the field’s name when validating against the provided input data.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• path (str | pathlib.Path)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod parse_obj(obj)

Return type:

Self

Parameters:

obj (Any)

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)

Return type:

Self

Parameters:

• b (str | bytes)

• content_type (str | None)

• encoding (str)

• proto (pydantic.deprecated.parse.Protocol | None)

• allow_pickle (bool)

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')

Return type:

Dict[str, Any]

Parameters:

• by_alias (bool)

• ref_template (str)

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)

Return type:

str

Parameters:

• by_alias (bool)

• ref_template (str)

• dumps_kwargs (Any)

classmethod update_forward_refs(**localns)

Return type:

None

Parameters:

localns (Any)

classmethod validate(value)

Return type:

Self

Parameters:

value (Any)

earned: str

total: str